BRS Labs - The Market

The Market

COGNITIVE VIDEO ANALYTICS

cog•ni•tion [kog-nish-uhn] noun

1. the mental process of knowing, including aspects such as awareness, perception, reasoning, and judgment.

2. the product of such a process; something thus known, perceived, etc.

3. knowledge.

Why Video Surveillance Needs a Cognitive Approach

Using vision analytics algorithms for the purpose of assisting video surveillance systems to “see” better has been around for well over a decade. Technologies have been built so that a camera can be programmed to look for, or watch for, a specific object or motion with varying degrees of success. These rules-based systems typically require extensive programming from humans, and from a scalability view, make it difficult for them to achieve broad market adoption. These systems are also typically riddled with false positives and become too manpower intensive to set up and maintain. Developing a video analytics system that follows a path consistent with the cognitive process is a technological approach that produces more accurate results and does so in a way that continues to improve the effectiveness of any video surveillance system over time.

Cognitive Video Analytics Learns Just Like You and Me

As mentioned, the use of vision analytics algorithms in the area of video surveillance is not new and using the concepts around the cognitive sciences (the study of how the human brain functions) in the development of technology has been applied in many different applications and industries. However, creating an interconnection between vision analytics and a system that emulates the cognitive process, utilizing various machine intelligence and machine-learning technologies, represents a breakthrough for the video surveillance industry. A cognitive-based video analytics system is not only equipped with the ability to “see” better but also to “learn,” “remember,” and “make observations” much like a human brain. A cognitive-based video analytics system constructs its own understanding of the world it is observing by evaluating the patterns of activity for any given environment over time. A "mental model" is then created for each scene to make sense of observed activities. Learning is achieved by continuing to adjust the mental models to interpret and alert on new activities as they occur, within the context of previous activities. Thus, a cognitive-based system creates an understanding of what is seen through a camera’s field of view and establishes what it determines to be normal for any given environment. It is therefore able to alert on activity it determines to be abnormal.

Cognitive-Based vs. Rules-Based

Every environment and every scene is unique. No one is able to write enough rules to cover the infinite number of possibilities for any given environment. This is why it is important to have a cognitive-based system that is able to learn what is normal for every unique environment and then alert when there are activities that occur outside of that normal pattern. That same learning capability is also important in order to adapt to changes that may occur within any given environment over time. These two capabilities: the ability to adapt to almost any scene or environment and the ability to continue to improve upon its learning and alerting over time are the most important distinguishing factors of cognitive-based systems over rules-based video analytics systems. The benefits to businesses that adopt cognitive-based video analytics systems over rules-based systems can include everything from reduced cost due to less required coding and customization, increased effectiveness from reduced false positive alerting, and increased return on investment on the entire security infrastructure.

A Natural Evolution

Just as the information technology security business has evolved to include adaptive pattern-detection security solutions, so will video analytics and video management solutions. Building systems along these lines will produce the future capability to expand the use of video analytics systems across multiple cameras connected to the same system, such as providing the ability to track behavioral patterns from one camera to another. Future capability will also allow operators the ability to teach the system through “supervised” learning activities such as teaching the system that one alert included characteristics that will be consistently of interest to other security operators.

BRS Labs sees cognitive video analytics as the next stage in the natural evolution of video surveillance systems. Already deployed in security watch centers today, cognitive video analytics is enhancing security teams’ perception and awareness and will continue to increase the scalability and effectiveness of security operations over time.

Company Overview Executive Team The Market Industry News Press Careers		The Market COGNITIVE VIDEO ANALYTICS cog•ni•tion [kog-nish-uhn] noun 1. the mental process of knowing, including aspects such as awareness, perception, reasoning, and judgment. 2. the product of such a process; something thus known, perceived, etc. 3. knowledge. Why Video Surveillance Needs a Cognitive Approach Using vision analytics algorithms for the purpose of assisting video surveillance systems to “see” better has been around for well over a decade. Technologies have been built so that a camera can be programmed to look for, or watch for, a specific object or motion with varying degrees of success. These rules-based systems typically require extensive programming from humans, and from a scalability view, make it difficult for them to achieve broad market adoption. These systems are also typically riddled with false positives and become too manpower intensive to set up and maintain. Developing a video analytics system that follows a path consistent with the cognitive process is a technological approach that produces more accurate results and does so in a way that continues to improve the effectiveness of any video surveillance system over time. Cognitive Video Analytics Learns Just Like You and Me As mentioned, the use of vision analytics algorithms in the area of video surveillance is not new and using the concepts around the cognitive sciences (the study of how the human brain functions) in the development of technology has been applied in many different applications and industries. However, creating an interconnection between vision analytics and a system that emulates the cognitive process, utilizing various machine intelligence and machine-learning technologies, represents a breakthrough for the video surveillance industry. A cognitive-based video analytics system is not only equipped with the ability to “see” better but also to “learn,” “remember,” and “make observations” much like a human brain. A cognitive-based video analytics system constructs its own understanding of the world it is observing by evaluating the patterns of activity for any given environment over time. A "mental model" is then created for each scene to make sense of observed activities. Learning is achieved by continuing to adjust the mental models to interpret and alert on new activities as they occur, within the context of previous activities. Thus, a cognitive-based system creates an understanding of what is seen through a camera’s field of view and establishes what it determines to be normal for any given environment. It is therefore able to alert on activity it determines to be abnormal. Cognitive-Based vs. Rules-Based Every environment and every scene is unique. No one is able to write enough rules to cover the infinite number of possibilities for any given environment. This is why it is important to have a cognitive-based system that is able to learn what is normal for every unique environment and then alert when there are activities that occur outside of that normal pattern. That same learning capability is also important in order to adapt to changes that may occur within any given environment over time. These two capabilities: the ability to adapt to almost any scene or environment and the ability to continue to improve upon its learning and alerting over time are the most important distinguishing factors of cognitive-based systems over rules-based video analytics systems. The benefits to businesses that adopt cognitive-based video analytics systems over rules-based systems can include everything from reduced cost due to less required coding and customization, increased effectiveness from reduced false positive alerting, and increased return on investment on the entire security infrastructure. A Natural Evolution Just as the information technology security business has evolved to include adaptive pattern-detection security solutions, so will video analytics and video management solutions. Building systems along these lines will produce the future capability to expand the use of video analytics systems across multiple cameras connected to the same system, such as providing the ability to track behavioral patterns from one camera to another. Future capability will also allow operators the ability to teach the system through “supervised” learning activities such as teaching the system that one alert included characteristics that will be consistently of interest to other security operators. BRS Labs sees cognitive video analytics as the next stage in the natural evolution of video surveillance systems. Already deployed in security watch centers today, cognitive video analytics is enhancing security teams’ perception and awareness and will continue to increase the scalability and effectiveness of security operations over time.
Home \| Careers \| Contact \| Products \| Solutions \| Advantages ©2008 Behavioral Recognition Systems