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Video analytics add needed intelligence to body cameras

Director of Public Safety, Law Enforcement, Emergency Management Solutions, IBM
Business Unit Executive for Law Enforcement and Policing Solutions, IBM

The proliferation of video over the past several years has been nothing short of astonishing. Today, just about every event anywhere in the world seems to be captured on video by a security camera, smartphone camera or body camera. The number of devices that capture images has exploded. At the end of 2014, IHS Technology estimated over 245 million operational surveillance cameras were active globally, which is just a fraction of the total number of devices capturing video, and current numbers are estimated to increase significantly.

The use of body cameras, in particular, by public safety and law enforcement professionals is a hot topic as increasing number of agencies purchase equipment and set up new policies. These devices capture events from the officers’ perspective, and can be used as a tool to help discover ways to improve officer safety and enhancing methods for protecting the general public. 

Managing all that video

The cost of the cameras alone is the tip of the iceberg, as the implications of using them are far reaching and raise several questions. From a practical perspective, how and where do we store all this video? How do we ensure it has not been tampered with? How do we access it and search it? From a policy and legal perspective, how do we handle privacy issues? How do we distinguish and identify pertinent information on the video? How do we balance compliance between the Freedom of Information Act (FOIA)—which makes publicly collected information available to the general public—and Criminal Justice Information Standards (CJIS) requirements, which govern the handling and management of criminal information?

A solution is available to help with these video management questions and challenges. It falls in the realm of what is known as vision computing or video analytics. Video analytics can greatly assist law enforcement and public safety by revolutionizing how video and multimedia data are searched, tagged, used and managed. Video analytics adds intelligence to the video data collected by body cameras.

For more than 15 years, IBM has been working in the area of analyzing video captured by static cameras such as those used for monitoring traffic, closed-circuit television (CCTV) and surveillance. In addition, IBM has patented unique capabilities to interpret and index all the events captured in the camera’s field of view. For example, imagine a sophisticated engine that can automatically find and return instances of an individual or event matching a certain description. And at the same time, it can detect events and behaviors—for example, a person entering an off-limits area or leaving a bag unattended—and send alerts in near-real time. Unlike other such solutions, the IBM vision computing engine can handle millions of attributes and events from multiple streams of video.

Applying advanced analytics to video datahttp://www.ibmbigdatahub.com/sites/default/files/video-analytics-body-worn-cameras-blog.jpg

IBM’s experience and expertise and its continuing research in this area are now being applied to body cameras in law enforcement. This analytics technology helps optimize how the valuable video captured by body worn camera systems can be efficiently searched, retrieved and used effectively in criminal and internal investigations. Consider two scenarios illustrating the possibilities.

Rapid searching for individuals: Video analytics can make searching for suspects or potential threats simpler. For example, consider a scenario in which a suspect has been identified by an eyewitness. A video analytics tool can search hours of footage to look for a certain set of characteristics—hair color, baldness, head covering, glasses and skin tone—and other attributes such as clothing colors or patterns. Searching for these kinds of attributes is done automatically, and the search can be applied to video produced by many cameras. This capability can save the time and labor that would otherwise be required for an officer to view all the footage manually.

Redaction for meeting compliance requirements: Body cameras on law enforcement officers can capture all kinds of people, objects and activities in the course of an officer’s duties and responses to calls. The public or the media may make FOIA requests for some of this video footage. Intelligent video analytics enables police and public safety agencies to use a technique called redaction to help ensure that video provided to fulfill FOIA requests continues to comply with CJIS and privacy requirements. Redaction enables the agency to set up the criteria to automatically blur out images of minors, victims, confidential personal information, and other sensitive images that may have been captured by the camera’s lens. Manually performing such a task would be quite labor intensive, but automated redaction can significantly reduce the time and labor required to release video footage that is in compliance with the FOIA.

Today’s mainstream dialog around body-worn cameras is focused only on eyewitness accounting and the costs associated with storing and managing requirements for video. Realizing that the value of the video captured in this manner is not just in capturing it, but also in finding and using what is in the footage is equally important.

Meeting the needs of public agencies

IBM is well-positioned in the realm of video analytics to respond to the current needs of law enforcement and public safety. It is building on its more than 15 years of research and development experience, 10 years of production offerings worldwide and set of innovative patents. IBM will continue to invest in and drive innovation that can help unlock additional knowledge and insight that is contained in the hours of video collected by body cameras while helping to increase cost-effective management of video data to comply with government standards and policies.

Protect your citizens with advanced video analytics