Résumé
Video content analysis or video content analytics (VCA), also known as video analysis or video analytics (VA), is the capability of automatically analyzing video to detect and determine temporal and spatial events. This technical capability is used in a wide range of domains including entertainment, video retrieval and video browsing, health-care, retail, automotive, transport, home automation, flame and smoke detection, safety, and security. The algorithms can be implemented as software on general-purpose machines, or as hardware in specialized video processing units. Many different functionalities can be implemented in VCA. Video Motion Detection is one of the simpler forms where motion is detected with regard to a fixed background scene. More advanced functionalities include video tracking and egomotion estimation. Based on the internal representation that VCA generates in the machine, it is possible to build other functionalities, such as video summarization, identification, behavior analysis, or other forms of situation awareness. VCA relies on good input video, so it is often combined with video enhancement technologies such as video denoising, , unsharp masking, and super-resolution. Several articles provide an overview of the modules involved in the development of video analytic applications. This is a list of known functionalities and a short description. VCA is a relatively new technology, with numerous companies releasing VCA-enhanced products in the mid-2000s. While there are many applications, the track record of different VCA solutions differ widely. Functionalities such as motion detection, people counting and gun detection are available as commercial off-the-shelf products and believed to have a decent track-record (for example, even freeware such as dsprobotics Flowstone can handle movement and color analysis). In response to the COVID-19 pandemic, many software manufacturers have introduced new public health analytics like face mask detection or social distancing tracking.
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