FAQ | DS Box

Global shortage of higher cost of talent and staff with skills to monitor video surveillance 24/7/365, while exponential growth of user needs for video surveillance cameras/sensors / IoT devices connected to edge computing.

Today the market needs real-time action as prescriptive and not just descriptive and predictive, as the bottom line is to save cost by automating actions without additional investment of FTE (full-time equivalent employed person)

DS Box has a proven product, that is ready to plug and play with no additional investment and recurring cost of FTE, and with Appliance as a Service, it has up-sell and recurring revenues for system integrators with ready & growing installed base of Video Management Software (predicted to double every 5 years) and Video Data Analytics.
Businesses struggle to extract value from video and sensor data beyond surveillance. DS Box helps businesses make better use of data by extracting operationally useful insights. In a well-configured, fully integrated system, the real-time insights can provide operations teams with valuable real-time spatial intelligence.

However, integrating new generation hardware and software within legacy camera and sensor systems can be time-consuming, risky and relies on data processing in the Cloud. Processing in the Cloud has many downsides: added privacy and security risks, high latency, and additional costs. Simplifying and de-risking spatial intelligence solutions can help make this valuable technology as ubiquitous as the cameras and sensors themselves.
DS Box unlocks the full value of video and sensor data for businesses – placing real-time insights in the hands of operations teams. By moving spatial data analysis closer to the network edge, DS Box bypasses the cloud, improving speed, data privacy, and security. In addition, by offering our turnkey solution on subscription, DS Box derisks the procurement decision and simplifies implementation in even the most complex of operational environments.
From selling products to solutions to outcomes - The role of security cameras has moved beyond their primary goal of protecting assets at a site. Even though end customers are still slow to adopt the business intelligence part of security cameras, the technology is very much ready and continues to evolve at a rapid pace.

Perhaps this reluctance is because vendors have been selling solutions (earlier they were products) to customers. Our focus remains to deliver business outcomes to customers, and it is the only way to solve problems and explore opportunities within security and beyond security

“At its core, the term 'outcome' refers to creating value for customers."

For instance, traffic management using technology has been about monitoring speeding and issuing tickets, monitoring red light violations, etc., in the past. But technology of the future will even help factors like reducing pollution, reducing congestion by redirecting traffic, predicting traffic patterns that will help emergency response vehicles know where an accident might occur, and so on.

The customer doesn’t know what they don’t know. If all this sounds too futuristic for a customer, that is because it is. DS Box clearly knows that end customers will not understand the potential of DS Box to add value to their enterprises until they experience it. And this is not anything new to innovation.

Customers are aware of their problems, but not necessarily the best solution. This is an area where it intends to work with partners and end customers so that there is more awareness about the video DS Box and its benefits.

The DS Box goal is to leverage the possibilities of complex technologies, in the simplest way possible, while ensuring the existing hardware is fully utilised to enable effective security solutions for users.

Design a safer environment to protect people, and maintain productivity by turning to DS Box to meet new workplace requirements, and ensure employee and customer well-being, most importantly, safety and security is a matter of using technology in the right way.
DS Box ecosystem is a rapidly growing industry. With the increasing demand for real-time monitoring and security systems, there has been a significant surge in the deployment of video surveillance systems. Moreover, the emergence of advanced technologies such as video analytics and edge computing has further fuelled the growth of this market.

The global video surveillance market was valued at $42.8 billion in 2020 and is projected to reach $74.6 billion by 2025, growing at a CAGR of 11.8% during the forecast period (2021-2025). The increasing demand for video surveillance systems in various industries such as retail, healthcare, and banking, and the need for advanced security solutions are the major factors driving the growth of this market.

The video analytics market is also witnessing significant growth, and it is expected to reach $13.2 billion by 2025, growing at a CAGR of 21.5% during the forecast period. The increasing adoption of artificial intelligence and machine learning-based video analytics solutions, coupled with the need for real-time analytics, is driving the growth of this market.

The edge computing market is also gaining traction, and it is projected to reach $43.4 billion by 2027, growing at a CAGR of 34.1% during the forecast period. The increasing demand for low-latency data processing and the need for real-time analytics are the major factors driving the growth of this market.

The market is witnessing growth in various regions such as North America, Europe, Asia-Pacific, and Rest of the World (RoW).

The Asia-Pacific region is witnessing significant growth, and it is expected to register the highest CAGR in the next 10 years. The increasing adoption of video surveillance and video analytics solutions in various industries, such as retail, transportation, and healthcare, is driving the growth of this market in this region.
Despite the rapid growth, there are several challenges that could hinder its growth in the future. Some of these challenges are:

High initial costs: One of the major challenges in the adoption of all solutions is the high initial costs associated with these technologies. The cost of deploying and maintaining these systems can be significant, especially for small and medium-sized organisations.

Lack of skilled workforce: Another challenge is the shortage of skilled workforce. The implementation and maintenance of these technologies require specialized skills and expertise. The shortage of such skilled professionals could hinder the growth of this market.

Data privacy and security concerns: The increasing use has raised concerns about data privacy and security. The potential misuse of data captured by these systems could lead to breaches of privacy and security.

Compatibility issues: The lack of compatibility between different solutions is another challenge faced by this market. The integration of these technologies with existing systems and platforms can be a complex process, and compatibility issues could hamper the adoption of these technologies.

Legal and regulatory compliance: The deployment is subject to legal and regulatory compliance. Compliance with various regulations, such as GDPR, can be a challenge for organisations that operate in multiple jurisdictions.

Infrastructure limitations: The deployment of video surveillance, video analytics, and edge computing solutions requires a robust infrastructure. The lack of infrastructure in certain regions or countries could limit the adoption of these technologies.

DS Box aims to address these challenges in the video surveillance, video analytics, and edge computing market. With a clear focus on developing cost-effective solutions, education, certification by training, and ensuring data privacy and security, addressing compatibility issues, complying with legal and regulatory requirements, with a go-to-market strategy of Demo Units and offerings of “Appliance as a Service” model of one-stop service of these technologies.
There are several reasons why customers pay for insights and outcomes:

Business insights: DS Box can provide businesses with valuable insights. This information can help companies improve their products and services, optimise their organisational strategies, and enhance the overall customer experience.

Competitive advantage: By leveraging insights, companies can gain a competitive advantage by identifying trends and patterns before their competitors do. This can help them stay ahead of the curve and make more informed business decisions.

ROI: Customers may be willing to pay for insights and outcomes because they believe it will provide a positive return on investment (ROI). By using data to optimise their business operations and improve customer engagement, companies can increase revenue, reduce costs, and improve their bottom line.

Customisation: It can be used to create personalised experiences for customers, which can increase engagement and loyalty. By understanding customer preferences and behaviour, organisations can tailor their products and services to meet their needs and expectations.

Efficiency: By making data prescriptive in decision-making processes, the insights can help companies operate more efficiently and effectively. This can save time and resources while improving the management, safety, and sustainability.
Making sense involves several steps, including:

Define your problem: Determine what you want to accomplish and how data can help you achieve your goal. Be specific about the questions you want to answer or the insights you want to gain.

Capture and manage video and sensor data with Video Management Software: Ensure the right position of cameras and sensors to generate accurate feeds for analytics.

Actionable data: Define rules for analysis and define your actions, and communicate as alerts.

Video Data Analytics Software: Use the right analytical tools to identify patterns, relationships, and trends in your data. This can involve descriptive statistics, data visualisation, and machine learning.

Interpret your results: Draw conclusions from your analysis and use them to inform decision-making. Consider the context of your data and any limitations of your analysis.

Communicate your findings: Present your insights in a clear and concise manner to stakeholders who can use them to inform their work. This may involve creating reports, dashboards, or other visualisations.

Monitor and evaluate your results: Regularly assess the impact of your data analysis on your problem-solving efforts. This can help you refine your approach and improve your outcomes over time
Video management software (VMS) is a type of software that provides centralised management and storage of digital video footage captured by surveillance cameras. VMS typically offers features such as live viewing, playback, video recording, event management, and access control. The software can be used for a range of purposes, including security, traffic management, and monitoring industrial processes. VMS is usually used in conjunction with a network of cameras and other hardware components to provide a complete surveillance solution.
Video management software (VMS) can help DS Box in the following ways:

Real-time processing: VMS software can be seamlessly integrated to perform real-time video processing on edge devices like DS Box, rather than in a central server. This reduces the latency and bandwidth requirements associated with transmitting video data over the network.

Using Edge: DS Box can take data feeds from other storage or edge devices like cameras and sensors equipped with VMS, it can process video footage locally, reducing the reliance on central servers for sending video for storage and retrieval. This also helps to ensure that video data is generating insights, even in the event of network connectivity issues.

Decentralised management: With DS Box edge computing and VMS, video data can be managed and analysed at the edge, reducing the need for centralised management and allowing for more efficient and effective video surveillance.

Improved security: By processing video data at the edge, VMS can leverage this to improve the security of the video surveillance system by reducing the risk of data breaches and unauthorised access to sensitive video data.

Scalability: DS Box allows VMS to scale to meet the demands of large and complex video surveillance systems, as processing and storage can be distributed across multiple edge devices.
Yes, DS Box can generate real-time insights. Since DS Box is processing data locally, at or near the source of data generation, rather than in a centralised server. This can enable real-time insights by allowing data to be analysed and acted upon quickly and efficiently.

For example, in an industrial setting, DS Box can be used to perform real-time analysis of sensor data to identify potential equipment failures, enabling preventive maintenance to be performed before a failure occurs. In a video surveillance system, DS Box can be used to perform real-time video analysis to detect security threats or other incidents of interest.

Real-time insights are becoming increasingly important in many fields, as they allow organisations to make decisions and take actions more quickly and effectively. DS Box can play a critical role in enabling real-time insights by providing the processing power and network connectivity necessary to analyse and act on data in near real-time.
Video data analytics is the process of analysing digital video footage to extract meaningful insights and information. This can involve using algorithms and machine learning techniques to automatically identify and track objects, analyse behaviours and patterns, and detect unusual events or anomalies. Video data analytics can be used for a range of purposes, including security, traffic management, customer behaviour analysis, and process optimisation.

For example, in a security context, video data analytics can be used to identify and track individuals, detect potential security threats, and automatically trigger alerts or responses when a threat is detected. In a retail setting, video data analytics can be used to track customer behaviour and movement, providing insights into customer preferences and habits. In industrial settings, video data analytics can be used to monitor and optimise production processes, detecting and correcting problems in real-time.

Video data analytics can provide significant benefits by enabling organisations to make more informed decisions and improve their operations based on data-driven insights. The technology is becoming increasingly important as organisations seek to make the most of the growing volumes of video data being generated in a wide range of industries.
Real-time insights are valuable because they allow organisations to take timely and effective action based on the data they collect. Some of the benefits of real-time include:

Improved decision making: Real-time provides organisations with timely and accurate information, allowing them to make more informed decisions and respond quickly to changes in their environment.

Increased efficiency: By processing in real-time, organisations can identify and respond to issues and opportunities more quickly, improving the efficiency of their operations.

Enhanced security: In security applications, it can be used to detect and respond to security threats in near real-time, helping to prevent incidents from occurring and improving overall security.

Improved customer experience: In retail and other customer-facing industries, it can be used to track customer behaviour and preferences, providing valuable insights into how to improve the customer experience.

Real-time monitoring and control: It can be used to monitor and control industrial processes, enabling organisations to detect and correct problems in real-time and improve overall efficiency.

Overall, it provides organisations with valuable insights that can be used to improve decision making, increase efficiency, enhance security, and improve the customer experience. The real-time nature of the technology is particularly important in many applications, as it allows organisations to respond quickly in real-time.
DS Box Appliance as a Service (AaaS) is a delivery model for hardware and software products that combines the convenience of a cloud-based service or hybrid or standalone with the performance and security of on-premise solutions. AaaS typically involves the use of a physical appliance or device, that is located on the customer's premises. The appliance is managed and maintained by the service provider, who provides the hardware, software, and support services as a single, integrated solution.

The AaaS model provides a number of benefits compared to traditional on-premise solutions, including:

Lower upfront costs: AaaS eliminates the need for customers to invest in expensive hardware and software upfront, as the costs are spread over the life of the service contract.

Reduced operational costs: By outsourcing the management and maintenance of the appliance to the service provider, customers can reduce their operational costs and free up valuable internal resources, and shorter sales cycles.

Improved performance and security: With an AaaS solution, the appliance is typically located on the customer's premises, providing the performance and security benefits of an on-premise solution.

Scalability: AaaS solutions are typically scalable, allowing customers to increase or decrease their usage as needed without having to make additional investments in hardware and software.

AaaS is becoming an increasingly popular delivery model for hardware and software products, particularly in industries such as video surveillance, where the combination of performance, security, and cost-effectiveness is critical.
Video surveillance cameras are used by a wide range of organisations and individuals for various purposes, including:

Government: Government agencies, such as law enforcement, use video surveillance cameras to enhance public safety and monitor criminal activity.

Retail: Retail businesses use video surveillance cameras to deter theft, monitor customer behaviour, and improve store operations.

Banking and Financial Services: Banks and financial services companies use video surveillance cameras to improve security, monitor customer behaviour, and prevent fraud.

Healthcare: Healthcare organisations use video surveillance cameras to improve patient safety, monitor staff performance, and prevent theft or abuse.

Transportation: Transportation companies use video surveillance cameras to improve safety, optimise operations, and reduce costs.

Education: Educational institutions use video surveillance cameras to improve campus safety and monitor student behaviour.

Residential Properties: Homeowners and property managers use video surveillance cameras to improve security, monitor activity, and deter theft.

Sports and Entertainment: Sports teams and entertainment organisations use video surveillance cameras to improve fan experience, monitor crowd behaviour, and prevent criminal activity.
Video data analytics is used by a wide range of organisations and industries, including:

Retail: Retail businesses use video data analytics to optimise store layouts, track customer behaviour, and monitor inventory levels.

Healthcare: Healthcare organisations use video data analytics to improve patient safety, monitor staff performance, and optimise patient flow.

Public Safety: Law enforcement agencies and other public safety organisations use video data analytics to enhance their surveillance capabilities and improve response times.

Transportation: Transportation companies use video data analytics to optimise fleet management, improve safety, and reduce costs.

Education: Educational institutions use video data analytics to improve campus safety and monitor student behaviour.

Manufacturing: Manufacturing companies use video data analytics to optimise production processes, improve quality control, and reduce costs.

Gaming: Gaming companies use video data analytics to optimise player engagement, monitor player behaviour, and improve game performance.

Banking and Financial Services: Banks and financial services companies use video data analytics to improve security, monitor customer behaviour, and enhance marketing efforts.

Sports and Entertainment: Sports teams and entertainment organisations use video data analytics to optimise performance, improve the fan experience, and generate new revenue streams.
Video surveillance cameras are feeding real-time data, and DS Box makes sense of the data for several reasons, including:

Enhanced Security: It can help detect and prevent security breaches, monitor suspicious behaviour, and improve overall security.

Improved Operations: It can provide valuable insights into operations, allowing organisations to optimise processes, reduce costs, and improve overall efficiency.

Real-Time Insights: It can provide real-time insights, allowing organisations to respond quickly to changing conditions and improve decision making.

Increased Productivity: It can help identify areas for improvement, reduce downtime, and improve overall productivity.

Better Customer Experience: It can help organisations better understand customer behaviour and preferences, allowing them to enhance the customer experience.

Fraud Detection: It can help organisations detect and prevent fraud, protecting against financial losses and improving overall security.

Better Data Management: It can help organisations better manage video data, making it easier to access and analyse the information they need.
DS Box can help in perimeter protection by automatically analysing video footage to detect potential security threats and alert security personnel in real-time. Some of the ways that it can help in perimeter protection include:

Object detection and tracking: It can be used to detect and track individuals or objects, such as vehicles or packages, in real-time. This can help security personnel to identify potential security threats and respond quickly.

Intrusion detection: It can be used to detect unauthorised entry into restricted areas, such as a secure perimeter or a secure building. The technology can be configured to detect specific types of intrusion, such as jumping over a fence, or to trigger an alarm when an intrusion is detected.

Loitering detection: It can be used to detect individuals or vehicles that are loitering in a specific area, such as near a secure perimeter, for an extended period of time. This can help to identify potential security threats and trigger an alert.

Abnormal behaviour detection: It can be used to detect unusual or suspicious behaviour, such as individuals carrying large bags or acting in a threatening manner. This can help security personnel to respond quickly to potential security threats.

Traffic flow analysis: It can be used to analyse traffic flow at perimeter access points, such as gates or checkpoints, helping to identify potential security threats and improve the efficiency of security operations.

DS Box provides an effective solution for perimeter protection by enabling organisations to detect potential security threats in real-time and respond quickly to potential incidents. The technology is becoming increasingly important as organisations seek to improve the security and efficiency of their perimeter protection operations.
DS Box can help in healthcare by providing valuable insights into patient behaviour, staff activity, and operational efficiency. Some of the ways that it can help in healthcare include:

Patient monitoring: It can be used to monitor patients in real-time, providing healthcare providers with valuable information about patient behaviour and well-being. For example, video analytics can be used to detect changes in a patient's behaviour or posture, alerting staff to potential problems.

Staff monitoring: It can be used to monitor staff behaviour, such as how often they interact with patients, how long they spend at their station, and how they perform tasks. This information can be used to improve staff efficiency and effectiveness.

Asset tracking: It can be used to track the movement of equipment and supplies, such as wheelchairs and medication carts, helping to improve operational efficiency and reduce waste.

Queue management: It can be used to monitor waiting areas and queues, helping to reduce wait times and improve the patient experience.

Facility management: It can be used to monitor the use of space in healthcare facilities, helping to optimise the use of resources and improve the overall efficiency of the facility.

DS Box is a valuable tool for healthcare providers, enabling them to monitor patient behaviour, staff activity, and operational efficiency. The technology provides valuable insights that can be used to improve patient care, enhance the efficiency of healthcare operations, and reduce waste.
There are several challenges of healthcare in patient monitoring at care homes and hospitals, some of which include:

Staffing shortages: One of the biggest challenges in patient monitoring is a shortage of healthcare staff, which can lead to inadequate patient monitoring and delays in identifying and addressing critical changes in a patient's condition.

Technological limitations: Many care homes and hospitals use outdated or inadequate technology for patient monitoring, which can make it difficult to track or monitor patient condition accurately and in real-time.

Patient privacy: Patient monitoring often involves the use of technology that collects and transmits sensitive health data. Ensuring the privacy and security of this data is essential to maintaining patient trust.

Communication gaps: In large care homes and hospitals, there can be communication gaps between different departments or shifts, which can lead to delays or errors in patient monitoring and care.

Patient acuity: Patients in care homes and hospitals can have complex medical needs, which can make it challenging to monitor them adequately and quickly identify changes in their condition.

Cost: Implementing and maintaining effective patient monitoring systems can be expensive, and care homes and hospitals may not have the resources to invest in the latest technology or hire additional staff.

Patient mobility: Patients in care homes and hospitals may have limited mobility, making it difficult to monitor them effectively using video surveillance cameras and sensors or move them to different areas of the facility.Top of Form
DS Box can help in the workplace by providing valuable insights into employee behaviour, operational efficiency, and security. Some of the ways that video analytics can help in the workplace include:

Employee monitoring: It can be used to monitor employee behaviour, such as how often they interact with customers, how long they spend at their station, and how they perform tasks. This information can be used to improve employee efficiency and effectiveness.

Customer tracking: It can be used to track customer behaviour, such as how long they spend in the store, which products they are interested in, and which areas of the store they visit. This information can be used to improve the customer experience and drive sales.

Asset tracking: It can be used to track the movement of equipment and supplies, such as delivery trucks and inventory, helping to improve operational efficiency and reduce waste.

Queue management: It can be used to monitor waiting areas and queues, helping to reduce wait times and improve the customer experience.

Security: It can be used to monitor the workplace for potential security threats, such as unauthorised access or theft. The technology can be configured to detect specific types of security incidents, such as individuals entering restricted areas, and trigger an alarm when an incident is detected.

DS Box is a valuable tool for organisations, enabling them to monitor employee behaviour, customer activity, and operational efficiency. The technology provides valuable insights that can be used to improve employee performance, enhance the customer experience, and improve the overall efficiency of the workplace.
DS Box can help in smart cities by providing valuable insights into urban activity, traffic patterns, and public safety. Some of the ways that video analytics can help in smart cities include:

Traffic management: It can be used to monitor traffic patterns, such as vehicle count, speed, and direction. This information can be used to optimise traffic flow, reduce congestion, and improve the overall efficiency of the transportation network.

Public safety: It can be used to monitor public spaces, such as parks and sidewalks, for potential security threats, such as loitering or suspicious behaviour. The technology can be configured to detect specific types of security incidents, such as individuals carrying large bags or acting in a threatening manner, and trigger an alarm when an incident is detected.

Environmental monitoring: It can be used to monitor environmental conditions, such as air quality, temperature, and light levels, helping to improve the overall liveability of the city.

Waste management: It can be used to monitor waste management operations, such as trash collection and recycling, helping to optimise the use of resources and reduce waste.

Urban planning: It can be used to monitor the use of public spaces, such as parks and sidewalks, helping city planners to optimise the use of resources and improve the overall liveability of the city.

DS Box is a valuable tool for smart cities, enabling them to monitor urban activity, traffic patterns, and public safety. The technology provides valuable insights that can be used to improve the efficiency of city operations, enhance the liveability of the city, and improve public safety.
DS Box can help in transportation by providing valuable insights into vehicle activity, traffic patterns, and passenger behaviour. Some of the ways that video analytics can help in transportation include:

Traffic management: It can be used to monitor traffic patterns, such as vehicle count, speed, and direction. This information can be used to optimise traffic flow, reduce congestion, and improve the overall efficiency of the transportation network.

Safety: It can be used to monitor vehicle activity, such as reckless driving or lane departure, helping to improve road safety and reduce accidents.

Passenger behaviour: It can be used to monitor passenger behaviour, such as how long they wait for a bus or train, and which areas of the station they visit. This information can be used to improve the passenger experience and optimise the use of resources.

Asset tracking: It can be used to track the movement of vehicles, such as buses and trains, helping to improve operational efficiency and reduce waste.

DS Box is a valuable tool for transportation organisations, enabling them to monitor vehicle activity, traffic patterns, and passenger behaviour. The technology provides valuable insights that can be used to improve the efficiency of transportation operations, enhance the passenger experience, and improve road safety.
DS Box can help in education by providing valuable insights into student behaviour, classroom dynamics, and operational efficiency. Some of the ways that video analytics can help in education include:

Student engagement: It can be used to monitor student behaviour, such as how often they participate in class, how long they spend working on individual tasks, and how they interact with other students. This information can be used to improve student engagement and academic performance.

Classroom dynamics: It can be used to monitor classroom dynamics, such as the frequency of student-teacher interactions and the amount of time spent on different topics. This information can be used to improve teaching methods and optimise the use of class time.

Asset tracking: It can be used to track the movement of equipment and supplies, such as laptops and projectors, helping to improve operational efficiency and reduce waste.

Safety and security: It can be used to monitor school grounds and classrooms for potential safety and security threats, such as unauthorised access or theft. The technology can be configured to detect specific types of security incidents, such as individuals entering restricted areas, and trigger an alarm when an incident is detected.

Student attendance: It can be used to monitor student attendance, helping to improve accountability and reduce truancy.

DS Box is a valuable tool for educational institutions, enabling them to monitor student behaviour, classroom dynamics, and operational efficiency. The technology provides valuable insights that can be used to improve student engagement, optimise the use of resources, and enhance the overall safety and security of the educational environment.
DS Box can help in retail by providing valuable insights into customer behaviour, operational efficiency, and inventory management. Some of the ways that video analytics can help in retail include:

Customer behaviour: It can be used to monitor customer behaviour, such as the frequency of visits, dwell time, and the products that customers are interested in. This information can be used to improve the customer experience and optimise store layout and product placement.

Operational efficiency: It can be used to monitor store operations, such as queue management and staff performance. The technology can be used to identify areas where processes can be improved and to help optimise staffing levels.

Inventory management: It can be used to monitor the movement of products on store shelves and in the backroom, helping to improve inventory accuracy and reduce stock shortages.

Loss prevention: It can be used to monitor the store for potential security incidents, such as shoplifting, and to trigger an alarm when an incident is detected.

Sales analysis: It can be used to monitor sales transactions, helping to identify trends and optimise pricing strategies.

DS Box is a valuable tool for retailers, enabling them to monitor customer behaviour, operational efficiency, and inventory management. The technology provides valuable insights that can be used to improve the customer experience, optimise store operations, and enhance the overall security of the retail environment
DS Box can help in airports by providing valuable insights into passenger behaviour, operational efficiency, and security. Some of the ways that video analytics can help in airports include:

Passenger behaviour: It can be used to monitor passenger behaviour, such as the frequency of visits, dwell time, and the areas of the airport that passengers visit. This information can be used to improve the passenger experience and optimise the use of resources.

Operational efficiency: It can be used to monitor airport operations, such as queue management and staff performance. The technology can be used to identify areas where processes can be improved and to help optimise staffing levels.

Security: It can be used to monitor the airport for potential security incidents, such as unauthorised access or suspicious behaviour, and to trigger an alarm when an incident is detected.

Asset tracking: It can be used to track the movement of equipment and supplies, such as luggage carts and ground support vehicles, helping to improve operational efficiency and reduce waste.

Flight analysis: It can be used to monitor flight operations, such as flight departures and arrivals, helping to improve the efficiency of flight operations and reduce delays.

DS Box is a valuable tool for airports, enabling them to monitor passenger behaviour, operational efficiency, and security. The technology provides valuable insights that can be used to improve the passenger experience, optimise airport operations, and enhance the overall security of the airport environment.
DS Box can help in Industry 4.0 by providing valuable insights into industrial processes and equipment performance. Industry 4.0 refers to the fourth industrial revolution, which is characterised by the integration of advanced technologies, such as artificial intelligence, the internet of things, and cyber-physical systems, into industrial processes. Some of the ways that video analytics can help in Industry 4.0 include:

Process optimisation: It can be used to monitor industrial processes, such as production lines and machinery performance, and to identify areas where processes can be improved. The technology can help to optimise production, reduce downtime, and increase efficiency.

Equipment monitoring: It can be used to monitor the performance of equipment and to detect potential issues, such as wear and tear or breakdowns. The technology can help to reduce maintenance costs and increase equipment uptime.

Quality control: It can be used to monitor the quality of products and to detect potential defects, such as cracks or flaws. It can help to improve the quality of products and reduce the need for manual inspection.

Energy efficiency: It can be used to monitor energy usage and to identify opportunities for energy savings. It can help to reduce energy costs and increase sustainability.

Predictive maintenance: It can be used to monitor equipment performance and to predict when maintenance will be required, helping to reduce downtime and improve equipment uptime.

DS Box provides a valuable tool for Industry 4.0, enabling organisations to monitor industrial processes, equipment performance, and energy usage. The technology provides valuable insights that can be used to optimise production, reduce downtime, improve quality, and enhance the overall sustainability of industrial processes.
Edge computing can be both on-premise and in the cloud.

On-premise edge computing refers to the deployment of edge computing devices, such as gateways or edge servers, within a physical location, such as a factory or an office. This deployment model is often used when low latency and high security are required, or when there is limited connectivity to the cloud.

Cloud edge computing refers to the deployment of edge computing services within a cloud infrastructure. This deployment model is often used when there is high-speed connectivity to the cloud, and when scalability and cost-effectiveness are primary concerns.

The choice between on-premise and cloud edge computing depends on a variety of factors, including the specific requirements of the organisation, the type of data being processed, and the available resources. Both on-premise and cloud edge computing have their advantages and disadvantages, and organisations must carefully evaluate their specific needs before choosing a deployment model
Yes, edge computing can offer more computing power on-premises in real-time. Edge computing is designed to bring computing power closer to the source of data, enabling faster processing and lower latency. By deploying edge computing devices on-premises, organisations can take advantage of the increased computing power to process data in real-time. This is particularly useful for applications that require immediate processing and decision-making, such as real-time video analysis, IoT device management, and predictive maintenance.

For example, in a factory setting, edge computing devices can be deployed on-premises to monitor machinery performance and detect potential issues in real-time. The edge devices can process data from sensors and cameras in real-time, analyse the data, and trigger an alarm if an issue is detected. This can help to reduce downtime, improve efficiency, and increase equipment uptime.

Overall, edge computing offers the potential for increased computing power on premises in real-time, enabling organisations to process data and make decisions faster, with lower latency. This can help to improve operational efficiency, reduce downtime, and increase productivity
Edge computing for video analytics provides several advantages, including:

Real-time Processing: Edge computing enables video data to be processed at or near the source of the data, reducing latency and enabling real-time analytics.

Improved Data Privacy: By processing video data at the edge, sensitive data can be kept within an organisation's control, reducing the risk of data breaches and ensuring that privacy regulations are followed.

Increased Efficiency: By processing video data at the edge, bandwidth requirements are reduced and network traffic is optimised, improving network efficiency and reducing the load on central data centers.

Scalability: Edge computing makes it possible to scale video analytics systems to meet the needs of large deployments, such as those in smart cities or airports, without the need for expensive, centralised data centers.

Reduced Costs: Edge computing can help reduce the cost of video analytics systems by reducing the need for expensive data storage, network infrastructure, and cloud computing resources.

Improved User Experience: By processing video data at the edge, video analytics systems can deliver faster and more accurate insights, providing a better user experience for operators and end-users.

In summary, edge computing for video analytics enables real-time processing, improves data privacy and network efficiency, provides scalability and cost savings, and improves the overall user experience.
A CPU (Central Processing Unit) and a GPU (Graphics Processing Unit) are two different types of processors that are used for different types of computing tasks.

A CPU is the primary processing unit in a computer and is designed to handle a wide range of tasks, including general-purpose computing, control and management of the computer's operations, and data management. CPUs have a large number of simple processing units and are designed to handle tasks that can be broken down into a series of simple, independent operations.

A GPU, on the other hand, is designed specifically for graphics processing and is optimised for tasks that involve large amounts of data that can be processed in parallel. GPUs have a large number of simple processing units and are designed to handle tasks that can be broken down into a series of simple, independent operations.

The main difference between CPUs and GPUs is the way they are designed to handle tasks. CPUs are designed to handle a wide range of tasks with a focus on sequential processing, while GPUs are designed to handle a narrow range of tasks with a focus on parallel processing. As a result, GPUs are typically much faster than CPUs when it comes to tasks that involve large amounts of data that can be processed in parallel, such as video rendering, scientific simulations, and machine learning.

In summary, CPUs are general-purpose processors that are designed to handle a wide range of tasks, while GPUs are specialised processors that are optimised for tasks that involve large amounts of data that can be processed in parallel. The choice between a CPU and a GPU will depend on the specific requirements of the task being performed and the available resources.
Edge computing on-premises can use CPU computing for a variety of reasons.

Firstly, CPU computing is often used in edge computing devices because it provides a balance between performance and cost. Edge computing devices are typically deployed in remote locations and must be able to operate independently, so they need to be both powerful enough to handle a wide range of tasks and cost-effective to deploy. CPUs offer a good balance between performance and cost, making them a popular choice for edge computing devices.

Secondly, edge computing devices often need to handle a wide range of tasks, including data processing, data management, and control and management of the device's operations. CPUs are designed to handle a wide range of tasks, making them a good choice for edge computing devices that need to handle multiple tasks.

Finally, edge computing devices often need to be able to operate with limited connectivity to the cloud or other remote resources. CPUs are designed to operate independently, making them a good choice for edge computing devices that need to operate in offline or disconnected environments.

Overall, the use of CPU computing in edge computing on-premises is driven by a combination of performance, cost, versatility, and independence. CPUs offer a good balance between these factors, making them a popular choice for edge computing devices.
DS Box is integrating VMS (Video Management Software) and VAS (Video Analytics Software) to generate real-time insights because DS Box is a powerful computing designed to process, analyse, and make sense of large amounts of video data in real-time.

It typically involves using advanced algorithms and machine learning techniques to analyse video data in real-time, extracting relevant information and insights from the data as it is captured. This information is then used to generate real-time insights, such as identifying patterns and trends, detecting anomalies and incidents, and tracking the behaviour of people and objects.

DS Box is powered by an Intel CPU to process large amounts of video data, processing it securely and making it easily accessible for analysis. This allows organisations to process and analyse video data in real-time, generating real-time insights for decision-making and driving business outcomes.

Overall, the combination of DS Box using Intel CPU, VMS, and VAS can generate real-time insights by processing and analysing large amounts of video data in real-time, using advanced algorithms and machine learning techniques to extract relevant information and generate valuable insights. These real-time insights can help organisations make informed decisions and drive better business outcomes.
DS Box near the edge of video surveillance cameras in several ways:

Real-time processing: It can process video data in real-time, without the need for a connection to a remote server or the cloud. This allows organisations to quickly detect and respond to incidents and anomalies captured by the cameras.

Reduced network bandwidth usage: By processing video data, organisations can reduce the amount of data that needs to be transmitted to the cloud or a remote server. This can help to minimise network bandwidth usage and reduce the cost of transmitting large amounts of video data.

Improved security: It can process sensitive video data on-premise, which can help organisations to maintain control over their data and reduce the risk of data breaches.

Increased reliability: DS Box is designed to operate in remote and challenging environments, which can help organisations to ensure the continuous generation of insights from their video surveillance cameras.

Improved video analytics: By processing video data, organisations can leverage the processing power and capabilities of all devices to run advanced video analytics algorithms and generate real-time insights.

DS Box on premise can help organisations to improve the performance, beyond security, and real-time insights from their video surveillance cameras, while reducing the cost and complexity of transmitting large amounts of video data to the cloud or a remote server.
Edge computing has wide range of use cases across various industries, including:

Internet of Things (IoT): Edge computing can be used to process and analyse data generated by IoT devices in real-time, without the need for a connection to the cloud. This can help organisations to quickly respond to IoT device data, minimise network bandwidth usage, and improve the performance and reliability of IoT applications.

Video Surveillance: Edge computing can be used to process and analyse video data captured by surveillance cameras in real-time, without the need for a connection to the cloud. This can help organisations to quickly detect and respond to incidents and anomalies captured by the cameras, while reducing the cost and complexity of transmitting large amounts of video data to the cloud.

Industry 4.0: Edge computing can be used to process and analyse data generated by industrial IoT devices in real-time, without the need for a connection to the cloud. This can help organisations to quickly respond to industrial IoT device data, minimise network bandwidth usage, and improve the performance and reliability of Industry 4.0 applications.

Autonomous vehicles: Edge computing can be used to process and analyse data generated by autonomous vehicles in real-time, without the need for a connection to the cloud. This can help organisations to quickly respond to autonomous vehicle data, minimise network bandwidth usage, and improve the performance and reliability of autonomous vehicle applications.

Healthcare: Edge computing can be used to process and analyse data generated by medical devices in real-time, without the need for a connection to the cloud. This can help organisations to quickly respond to medical device data, minimise network bandwidth usage, and improve the performance and reliability of healthcare applications.

These are just a few examples of the many use cases for edge computing. Edge computing can be used to process and analyse data generated by a wide range of devices and applications, making it a versatile and powerful technology for organisations across various industries.
Yes, edge computing can store data. Edge computing devices, such as edge servers, gateways, and routers, typically have built-in storage capabilities, allowing them to store data locally. This can be useful in scenarios where a connection to the cloud or a remote server is not available, or when organisations want to store sensitive data on-premise for security and privacy reasons.

Storing data locally at the edge can also help to minimise network bandwidth usage and reduce the cost of transmitting large amounts of data to the cloud or a remote server. Additionally, edge computing devices can be designed to operate in remote and challenging environments, making them well-suited for storing data in challenging conditions.

However, it's worth noting that the storage capacity of edge computing devices is typically limited, and organisations may need to consider additional storage solutions, such as cloud storage or network-attached storage (NAS), if they need to store large amounts of data.
Meta data is data that describes other data. It is information about a particular piece of data, such as a file or a database record, that provides additional context and helps to categorise and manage the data.

For example, meta data for a photo might include information such as the date the photo was taken, the camera used to take the photo, and the location where the photo was taken. Meta data for a video file might include information such as the video format, length, and resolution.

Meta data can be used for a variety of purposes, including search and discovery, indexing, classification, organisation, and management. In the context of video data analytics, meta data can be used to extract information such as the number of people in a video, the type of objects present in the video, and the movement patterns of people and objects in the video. This information can then be used to generate real-time insights and drive business decisions.
Real-time insights and outcomes can help organisations save costs in several ways, including:

Improved Efficiency: By providing real-time insights into operations, it can help organisations identify areas for improvement and optimise processes, reducing downtime and improving overall efficiency.

Reduced Maintenance Costs: It can help organisations monitor equipment performance, identify potential issues before they become problems, and reduce maintenance costs.

Better Resource Allocation: By providing real-time insights into operations, it can help organisations better allocate resources, reducing waste and improving overall efficiency.

Early Problem Detection: It can help organisations detect problems early, allowing them to take proactive steps to address the issue before it becomes a more serious and costly issue.

Fraud Detection: It can help organisations detect and prevent fraud, reducing financial losses and improving overall security.

Customer Retention: By providing better insights into customer behaviour and preferences, it can help organisations enhance the customer experience, reducing customer churn and improving customer loyalty.

Increased Productivity: It can help organisations identify areas for improvement and increase overall productivity, reducing the need for additional staff or resources.

Overall, real-time insights and outcomes can help organisations save costs by improving efficiency, reducing maintenance costs, improving resource allocation, detecting problems early, and increasing productivity.
Video analytics software is a type of software that analyses video data to extract information and insights. It uses computer vision and machine learning algorithms to automatically analyse video feeds from cameras and provide real-time insights and outcomes. The software can be used for various applications, such as surveillance, traffic management, marketing and customer behaviour analysis, retail optimisation, and more. Video analytics software can analyse video data in real-time and provide insights such as detecting anomalies, tracking objects, identifying patterns, and generating alerts. These insights can help organisations improve operations, reduce costs, enhance security, and provide better customer experiences.
Sensor data refers to the raw output of a sensor, which is a device that measures a physical quantity and converts it into a signal that can be read by an electronic device. Sensors can measure a variety of physical quantities, including temperature, pressure, light, motion, and more. The signal generated by the sensor is usually digital or analog, and it represents the physical quantity that was measured.

Sensor data can be used in many applications, such as industrial process control, environmental monitoring, health monitoring, and more. In these applications, the sensor data is collected, processed, and analysed to gain insights and make decisions. The insights can then be used to control processes, optimise systems, and improve outcomes.

In recent years, the rise of the Internet of Things (IoT) has led to an increased use of sensors, as more and more devices are equipped with sensors to collect data. This has resulted in a massive increase in the amount of sensor data that is generated and the need for software to process and analyse this data.
Yes, sensor data can be used for analytics. Sensor data is a rich source of information that can provide valuable insights into various systems and processes. The data collected by sensors can be analysed using various analytics techniques, such as statistical analysis, machine learning, and data mining, to uncover patterns, trends, and relationships that would otherwise be difficult to detect.

For example, in industrial settings, sensor data can be used to monitor equipment performance, detect faults, and optimise processes. In healthcare, sensor data from wearable devices can be used to track patient health and detect potential issues. In transportation, sensor data from vehicles can be used to optimise routes and reduce fuel consumption.

By analysing sensor data, organisations can make informed decisions and improve outcomes in a wide range of applications. The use of sensor data for analytics is becoming increasingly important as the number of connected devices grows and the amount of data generated increases
LIDAR (Light Detection and Ranging) is a technology that uses lasers to measure distances and gather information about the surrounding environment. LIDAR systems emit laser beams that bounce off objects and return to the sensor, providing information about the distance and shape of objects in the environment.

LIDAR technology is commonly used in autonomous vehicles, where it is used to create high-resolution 3D maps of the environment for navigation and obstacle detection. LIDAR is also used in a variety of other applications, including mapping, surveying, meteorology, and environmental monitoring.

LIDAR is different from other distance-sensing technologies, such as radar and sonar, in that it uses light, rather than radio waves or sound waves, to measure distances. This allows LIDAR to provide higher resolution and more accurate data, making it a valuable tool for a wide range of applications.
Spatial data intelligence refers to the process of analysing and interpreting data that has a geographic or spatial component. This type of data includes information about the location, shape, and movement of objects in the physical world, as well as information about the environment and the relationships between objects and their surroundings.

Spatial data intelligence is used in a wide range of fields, including GIS (Geographic Information Systems), location-based services, urban planning, and environmental science. In these fields, spatial data intelligence is used to create maps, analyse patterns and trends, and make informed decisions based on the geographic information contained in the data.

Spatial data intelligence involves the use of advanced technologies, such as GIS software, LIDAR (Light Detection and Ranging) sensors, and geographic information databases, to gather, process, and analyse spatial data. The results of spatial data intelligence can be visualised in the form of maps, 3D models, and other graphical representations that provide insights into the relationships and patterns in the data.

Overall, spatial data intelligence is a valuable tool for understanding and making decisions about the physical world, and for analysing the relationships and patterns in geographic data.
Video data analytics can generate spatial intelligence by analysing video footage and extracting information about the location, movement, and behaviour of objects and people within the footage. The extracted information is then used to create visual representations and insights about the patterns and relationships between objects and their surroundings. For example, in a security or surveillance setting, video analytics can be used to detect and track individuals, vehicles, and other objects within the video footage.

The information gathered from the video footage can then be used to create maps or 3D models showing the movement and behaviour of these objects over time, providing insights into the relationships between the objects and their surroundings.

In other applications, such as retail or traffic analysis, video analytics can be used to analyse patterns of behaviour and movement within a specific environment. This information can be used to identify trends and patterns in customer behaviour, traffic patterns, or other areas of interest.

Overall, video data analytics can generate spatial intelligence by analysing the location, movement, and behaviour of objects within video footage and creating visual representations and insights about the relationships and patterns in this data.
DS Box can save the cost of full-time equivalent (FTE) employees by automating certain tasks that would otherwise require human labour. For example, it can be used to automate the following tasks:

Surveillance and security monitoring: It can be used to automatically detect and track individuals, vehicles, and other objects within video footage, reducing the need for manual monitoring.

Object recognition and classification: It can automatically recognise and classify objects within video footage, reducing the need for manual data entry and classification.

Event detection and analysis: It can automatically detect events, such as theft or loitering, within video footage, reducing the need for manual event analysis.

Traffic analysis: It can automatically analyse traffic patterns and flow within video footage, reducing the need for manual traffic analysis.

By automating these tasks, it can reduce the need for FTE employees, thereby saving costs and improving efficiency. Additionally, it can provide more accurate and consistent results compared to manual processes, leading to improved decision-making and outcomes.
DS Box is transforming the way, we analyse video footage in real-time on edge.

DS Box is a powerful computing to analyse in edge and it leverages machine learning and computer vision techniques to automatically analyse video footage. With the increasing availability of video cameras, video data analytics has become an essential tool for organisations to extract meaningful insights and outcomes from the vast amounts of video data they generate.

Benefits of DS Box Video Data Analytics:

Improved Surveillance and Security: DS Box video data analytics can automatically detect and track individuals, vehicles, and other objects within video footage, enabling organisations to improve and leverage their surveillance and security efforts.

Automated Object Recognition and Classification: DS Box video data analytics can automatically recognise and classify objects within video footage, reducing the need for manual data entry and classification.

Enhanced Event Detection and Analysis: DS Box video data analytics can automatically detect events, such as theft or loitering, within video footage, enabling organisations to improve their event analysis and response times.

Accurate Traffic Analysis: DS Box video data analytics can automatically analyse traffic patterns and flow within video footage, providing organisations with accurate and actionable insights into their traffic patterns.

Reduced Costs: DS Box video data analytics can save the cost of full-time equivalent (FTE) employees by automating certain tasks that would otherwise require human labour, such as surveillance and security monitoring, object recognition, event detection, and traffic analysis.

Applications of DS Box Video Data Analytics:

Retail: DS Box video data analytics can be used to analyse customer behaviour and traffic patterns within retail stores, enabling organisations to optimise their store layouts and marketing strategies.

Healthcare: DS Box video data analytics can be used to monitor patient behaviour and activity within hospitals, enabling healthcare organisations to improve patient safety and satisfaction.

Transportation: DS Box video data analytics can be used to analyse traffic patterns and flow within transportation networks, enabling organisations to optimise their transportation systems and reduce congestion.

Education: DS Box video data analytics can be used to monitor classroom activity and student behaviour, enabling educators to improve their teaching practices and student outcomes.

DS Box is a powerful video data analytics and edge computing tool for organisations to extract meaningful insights and outcomes from their video footage. With its ability to automate certain tasks and provide accurate and actionable insights, DS Box is poised to transform the way organisations analyse video data.