Video analytics and streaming

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.

Relying on Video Analytics to automatically monitor cameras and alert for events of interest is in many cases much more effective than reliance on a human operator, which is a costly resource with limited alertness and attention. 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. Moreover, the TOSALL operator’s ability to monitor the video and effectively respond to events is significantly compromised as time goes by.

Video-analytics technology is transforming the Internet of Things and creating new opportunities. Are companies prepared to capture growth?

Some of the most innovative Internet of Things (IoT) applications involve video analytics—a technology that applies machine-learning algorithms to video feeds, allowing cameras to recognize people, objects, and situations automatically. These applications are relatively new, but several factors are encouraging their growth, including the increased sophistication of analytical algorithms and lower costs for hardware, software, and storage.

Today’s video analytics store “metadata” with every frame of video.  This metadata describes what the video analytics processed in real time.  This includes the number of targets in the scene, attributes related to each target (size, speed, color, direction of movement), as well as, more advanced features like objects entering or leaving, specific map locations, camera settings and information from collaborating sensors.  The result is the ability for the operator to perform very specific searches: “A car entering any entrance faster than 20 mph between the hours of 10:00 pm and 8:00 am,” and then receiving the associated video to these tightly constrained requests very quickly.  This is made possible through the fact that the software is now searching the metadata versus re-evaluating all the video.   In terms of efficiency and obtaining timely results during the highly stressful period following an incident, adding video forensic capability to your video analytic arsenal is well worth the investment