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        Spatio-temporal Classification of Aggression in Video Surveillance using Optical Flow History

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        Bacheloronderzoek KI Kevin Hardeman.pdf (1.080Mb)
        Publication date
        2018
        Author
        Hardeman, K.
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        Summary
        The role of automatic surveillance in modern society is rapidly in- creasing. While most of these systems operate by making a judgment of aggression based on two frames, in this paper we explore the effect of adding more frames to the quality of detecting aggression in video surveil- lance. The premise of doing this is that the system must be able to run in real-time, preferably using as little computing power as possible. We evaluate an algorithm by TNO that was developed with this goal in mind. The results show a significant increase in detecting aggressive instances when more frames are added. The tests were run on a dataset containing instances of street aggression supplied by the Dutch police. A history of 1.5 seconds worth of frames was found to deliver the best results on the dataset.
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        https://studenttheses.uu.nl/handle/20.500.12932/30705
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