SPECIAL SECTION: Video Analytics Promise Smarter Surveillance

Achieving an even mix of physical security and technology is an alchemy that is still being balanced. It involves blending three key factors: analytics and data fusion, convergence, and standards. These are the elements designers of security systems seek to realize for true situational awareness. The aim of this delicate balance is to be able to provide the most effective protection for any facility or application, be it a government agency, corporation, school, transportation agency, etc., while optimizing the efficiency of existing personnel and other resources.

Analytics offers the ability to mine data from different sensors, fuse it into a common data source and provide critical information to responders. With richer data, more detailed and accurate information can be extracted. Responders can receive a much more detailed picture of any possible threat. It is for this reason that integrating as many inputs as possible is critical for the highest degree of success with any security system.

The result is full situational awareness, or “total domain awareness” as it is commonly called by the Department of Homeland Security (DHS) and the Department of Defense (DOD). Whether this domain is a single building, a site with multiple structures or geographically dispersed locations, attaining the ideal of full situational awareness means that information and intelligence of different types (such as video, RFID and access control data) from various locations must be fully integrated on an ongoing basis.

The purpose is to send critical alarms and alerts to responders for their immediate attention. Ultimately, the purpose is not only to intercede effectively as problems occur but also to anticipate and prevent threats before they are carried out.

Analyzing video surveillance data is one of the leading strategies in the pursuit of this total domain awareness. The following discussion will concentrate on this technology and how the aforementioned three key factors apply.

Video Analytics Allows Responders to Detect Behavior Patterns
Video analytics is indeed a key element for achieving total situational awareness, but these systems alone do not constitute a whole solution. The primary role of video analytics systems is to provide information about one or more camera views or portions of a scene, in real-time or forensically.

When an observer is dependent on a single sensor or a group of disparate, unconnected sensors, it makes it difficult to “connect the dots” and detect common threads or patterns in events. Only when video data can be fused with data from other sources and sensors can observers begin to achieve the required levels of situational awareness.

For example, if someone is repeatedly loitering near a secure door, this may mean they will try to follow an authorized employee with credentials through a restricted entryway, a behavior known as tailgating. This type of behavior detected in not only one area, but throughout a facility or among several facilities would automatically precipitate an investigation and enable security personnel to take decisive action to stop a possible incident before it occurs.

Video analytics enables responders to detect a pattern of behavior emerging at multiple locations, and by fusing that data with an access control system, they are able to quickly determine who initiated the sequence that enabled an actual tailgate to occur.

3 Types of Analytics Are Video, IT Security and Access Control
Video analytics and the security analytics arena in which video plays a significant role are evolving rapidly.

Video surveillance systems were first deployed almost 40 years ago, consisting of analog cameras feeding videocassette recorders (VCRs) over dedicated coaxial cables. These so-called CCTV systems began an evolutionary shift the past decade. The introduction of digital video reorders (DVRs) and Internet protocol (IP)-based digital encoders and cameras opened the door to applying computer-based analytical tools to the video data.

The current generation of advanced digital video surveillance systems employs a network infrastructure that would be familiar to any IT professional.

Video analytics is one of three “silos” of analytics related to security today. The second is security analytics specific to IT. There is an enormous amount of research being done around mining data received in a network security environment to detect when an attack or unauthorized access is attempted on a computer system. The third is access control analytics, which includes such methods as iris scans and optical character recognition (OCR).

The ultimate goal is a mechanism that enables the merging of information from all three sources to achieve a comprehensive analytical view of a domain.

With the proliferation of digital systems, people are beginning to link video systems to other data-gathering devices to increase the data received about a specific event. Examples include linking a camera system with an OCR system to read a license plate and track a car, or linking an access control system with a camera system to make identifications when access control violations occur.

With their potential for embedded intelligence, the IP cameras of tomorrow will be able to run today’s intelligent video algorithms to automate a number of surveillance tasks, including detecting human behaviors, processing external sensor data, reading license plates, counting people, sounding alarms, opening or locking doors, turning lights on or off, and sending E-mail alerts with embedded images.

Security Software Being Designed to Integrate Within Enterprises
Video analytics vendors are providing part of what is likely to be an expanding set of integrated scanning and detection technologies. As security environments and IT converge, any surveillance system must be designed with integration in mind. In this way, security systems are starting to look a lot like traditional enterprise software environments by the way technologies “talk” to each other and how they manage crucial criteria.

Today, the architecture of a security system must take into account whether each element:

  • Is open standards and cross-platform compliant
  • Will integrate well using recognized enterprise software tools such as XML, Web services, etc.
  • Provides clear and consistent application programming interfaces (APIs) to enable integration

The ideal scenario for systems integrators is for them to be able to pick best-of-breed technologies in each area needed (video, infrared, RFID, etc.) and then feel confident they will integrate smoothly and happily coexist. For this reason, systems integrators are increasingly seeking solutions vendors that can partner with them to support such requirements as integrating new data sources and rapidly pushing events out to other systems.

Lack of Standards Makes Analytics Different for Different People
For systems integrators providing video analytics, one of the initial challenges is simply defining terms. Video analytics is often used to refer to anything from the simplest levels of analytics involving motion detection and object tracking through the most sophisticated behavior recognition systems.

Until industry groups and government agencies set standards, the terminology will remain in flux, confusing both buyers and sellers of video analytics. Ideally, organizations such as Underwriter’s Laboratories (UL), the Transportat
ion Security Administration (TSA), Sandia National Laboratories and others will establish common standards by which to judge and design digital security systems that include video analytics.

Until such standards are set, a rule of thumb for video analytics is that such a system must be able to:

  • Function in a variety of different situations and environments (such as wide-ranging weather and lighting conditions, and busy public areas)
  • Not just detect, but also classify anomalous events (is it a person crossing the tarmac or a large box blown by the wind?)
  • Structure and store the gathered data so it can be searched rapidly
  • Integrate with other sensor technologies and alert mechanisms

Intelligent Video Likely to Be the Future of Surveillance

Traditional object tracking or trip-wire approaches to video surveillance can be useful for limited purposes such as indicating whether anyone or anything enters a defined area. In more rapidly changing, visually busy situations like an airport terminal or public transportation facility, video analysis engines such as behavior recognition are required.

Sophisticated behavior recognition software available today – also generically called intelligent video – can recognize a human being, distinguish the person from an inanimate object and accurately determine the number of people in the camera view, where they are going, and where they have been. Video algorithms can identify specific types or sizes of vehicles, packages or pieces of luggage, and how long they have been stationary or removed from a location.

The algorithms can also detect a wide range of human behaviors and actions such as loitering, access control tailgating, perimeter intrusion and people who violate posted policy by walking the wrong direction in a checkpoint exit lane. Libraries of algorithms are continuously being expanded and new ones created to serve highly specialized surveillance requirements.

The system would then combine these capabilities with other sensors (biometrics, iris scan, radiation detectors and more) and apply data analytics to the entire universe of information.

Looking ahead, as the security and IT worlds converge, the physical environment will be protected with the same levels of security as the network world, and the two will be fully integrated for security purposes.

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