A Probabilistic Model for Surveillance Video Mining
Dai Ke-xue, Guohui Li, Ya-Li Gan · 2006
With the vast use of video surveillance systems, there are more and more video data. An exciting field called video mining is now putting forward which focuses on extracting semantic info, implicit patterns and knowledge from video data. In this paper, a surveillance video data mining approach is proposed to discover similar video segments from surveillance video through a probabilistic model. First, a simple background subtraction algorithm is utilized to get the binary mask of moving objects. So the motion of every frame is calculated to segment the sequence of surveillance video. Then a mixture of hidden Markov models using the expectation-maximization scheme is fitted to the motion data with some probability to identity the similar segments. Finally, abnormal events and meaningful patterns are mined. Experiments with real-time video demonstrate the promising potential of this approach