Paper
3 March 2014 A web-based video annotation system for crowdsourcing surveillance videos
Neeraj J. Gadgil, Khalid Tahboub, David Kirsh, Edward J. Delp
Author Affiliations +
Proceedings Volume 9027, Imaging and Multimedia Analytics in a Web and Mobile World 2014; 90270A (2014) https://doi.org/10.1117/12.2042440
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States
Abstract
Video surveillance systems are of a great value to prevent threats and identify/investigate criminal activities. Manual analysis of a huge amount of video data from several cameras over a long period of time often becomes impracticable. The use of automatic detection methods can be challenging when the video contains many objects with complex motion and occlusions. Crowdsourcing has been proposed as an effective method for utilizing human intelligence to perform several tasks. Our system provides a platform for the annotation of surveillance video in an organized and controlled way. One can monitor a surveillance system using a set of tools such as training modules, roles and labels, task management. This system can be used in a real-time streaming mode to detect any potential threats or as an investigative tool to analyze past events. Annotators can annotate video contents assigned to them for suspicious activity or criminal acts. First responders are then able to view the collective annotations and receive email alerts about a newly reported incident. They can also keep track of the annotators’ training performance, manage their activities and reward their success. By providing this system, the process of video analysis is made more efficient.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Neeraj J. Gadgil, Khalid Tahboub, David Kirsh, and Edward J. Delp "A web-based video annotation system for crowdsourcing surveillance videos", Proc. SPIE 9027, Imaging and Multimedia Analytics in a Web and Mobile World 2014, 90270A (3 March 2014); https://doi.org/10.1117/12.2042440
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CITATIONS
Cited by 8 scholarly publications.
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KEYWORDS
Video

Video surveillance

Surveillance

Video processing

Cameras

Machine learning

Machine vision

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