Video Analytics, also referred to as Video Content Analysis (VCA), is a generic term used to describe computerized processing and analysis of video streams. Computer analysis of video is currently implemented in a variety of fields and industries; however the term “Video Analytics” is typically associated with analysis of video streams captured by surveillance systems. Video Analytics applications can perform a variety of tasks ranging from real-time analysis of video for immediate detection of events of interest, to analysis of pre-recorded video for the purpose of extracting events and data from the recorded video (also known as forensic analysis).
Video Analytics Capabilities
- Dynamic masking- Blocking a part of the video signal based on the signal itself, for example because of privacy concerns.
- Motion detection- Motion detection is used to determine the presence of relevant motion in the observed scene.
- Object detection- Object detection is used to determine the presence of a type of object or entity, for example a person or car. Other examples include fire and smoke detection.
- Recognition-Face recognition and Automatic Number Plate Recognition are used to recognize, and therefore possibly identify persons or cars.
- Style detection- Style detection is used in settings where the video signal has been produced, for example for television broadcast. Style detection detects the style of the production process.
- Tamper detection- Tamper detection is used to determine whether the camera or output signal is tampered with.
- Video tracking- Video tracking is used to determine the location of persons or objects in the video signal, possibly with regard to an external reference grid.
How It Works
Video analytics use computer processing power to analyze the differences between one video image and the next. Pixels that are different between the two images being compared are grouped into objects. This process is called segmentation. The objects and their movement are then compared to preset behavioral and motion parameters, and alarm sequences are initiated if certain criteria are met or exceeded.
The accuracy of any video analytic system is directly related to the quality of the video images being processed. For the video analytic system to be reliable, the image streams must be of the highest quality.
Systems enabled with video analytics work on two key concepts:
Motion Detection: By examining each pixel in the frame, the video analytics software is able to pick up even the slightest movement.
Pattern Recognition: Video analytics help distinguish objects within a video frame. Specific patterns/objects can be programmed, which will be recognized within the frame. Should any change happen, i.e. object is moved, goes missing, or new object added; the software immediately recognizes it and sends out an alert. Typical applications of Video Analytics in security and surveillance include:
- License Plate Recognition
- Security Access Point Monitoring
- Perimeter Protection / Intrusion Detection
- Abandoned Object
- Object Removal
- Camera Tampering
Why Video Analytics
1. Video Analytics for Real-Time Alerts
- Penetration of unauthorized people / vehicles into restricted areas
- Tailgating of people / vehicles through secure checkpoints
- Traffic obstacles
- Unattended objects
- Vehicles stopped in no-parking zones, highways or roads
- Removal of assets
- Crowding or grouping
2. Video Analytics for Investigation
- Pinpoint an event in recorded video, and retrieve the relevant video segment from the stored video
- Perform analysis of motion patterns and detection of motion irregularities in defined areas
- Perform a variety of statistical analysis tasks relating to people or vehicles over defined periods of time