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.

Video analytics is used for automated surveillance. Smart cameras with analytics continuously analyze video and can detect the presence of people and vehicles and interpret their activities. Suspicious activities such as loitering or moving into an unauthorized area are automatically flagged and forwarded to security personnel.

Various research studies and real-life incidents indicate that an average human operator of a surveillance system, tasked with observing video screens, cannot remain alert and attentive for more than 20 minutes. Similar to human vision, which has a perceptual and cognitive aspect, video analytics uses computer vision algorithms which enable it to perceive or see, and machine intelligence to interpret, learn and draw inferences. The goal of video analytics is scene understanding, which differs from motion detection. In addition to detecting motion, analytics qualifies the motion as an object, understands the context around the object, and is able to track the object through the scene.