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