Video content analysis

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,^[1] video retrieval and video browsing,^[2] health-care, retail, automotive, transport, home automation, flame and smoke detection, safety, and security.^[3] 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^[4] and egomotion estimation.^[5]

Based on the internal representation that VCA generates in the machine, it is possible to build other functionalities, such as video summarization,^[6] 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, image stabilization, unsharp masking, and super-resolution.^{[citation needed]}

^ KINECT Archived September 12, 2010, at the Wayback Machine, add-on peripheral for the Xbox 360 console
^ Dimitrova, Nevenka, et al. "Applications of video-content analysis and retrieval." IEEE multimedia 9.3 (2002): 42-55.
^ VCA usage increase in British Security Archived 2014-03-16 at the Wayback Machine, BSIA report
^ Cavaliere, Danilo, Vincenzo Loia, and Sabrina Senatore. "Towards an ontology design pattern for UAV video content analysis." IEEE Access 7 (2019): 105342-105353.
^ Cavaliere, Danilo; Loia, Vincenzo; Saggese, Alessia; Senatore, Sabrina; Vento, Mario (2019-08-15). "A human-like description of scene events for a proper UAV-based video content analysis". Knowledge-Based Systems. 178: 163–175. doi:10.1016/j.knosys.2019.04.026. ISSN 0950-7051. S2CID 155625544.
^ Ma, Yu-Fei, et al. "A user attention model for video summarization." Proceedings of the tenth ACM international conference on Multimedia. 2002.

[KINECT-1] KINECT Archived September 12, 2010, at the Wayback Machine, add-on peripheral for the Xbox 360 console

[2] Dimitrova, Nevenka, et al. "Applications of video-content analysis and retrieval." IEEE multimedia 9.3 (2002): 42-55.

[VCA_in_Security-3] VCA usage increase in British Security Archived 2014-03-16 at the Wayback Machine, BSIA report

[4] Cavaliere, Danilo, Vincenzo Loia, and Sabrina Senatore. "Towards an ontology design pattern for UAV video content analysis." IEEE Access 7 (2019): 105342-105353.

[5] Cavaliere, Danilo; Loia, Vincenzo; Saggese, Alessia; Senatore, Sabrina; Vento, Mario (2019-08-15). "A human-like description of scene events for a proper UAV-based video content analysis". Knowledge-Based Systems. 178: 163–175. doi:10.1016/j.knosys.2019.04.026. ISSN 0950-7051. S2CID 155625544.

[6] Ma, Yu-Fei, et al. "A user attention model for video summarization." Proceedings of the tenth ACM international conference on Multimedia. 2002.

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