Motion clustering for similar video segments mining

To discover similar video segments from surveillance video sequence, a new approach is proposed for clustering motion data of moving objects. A simple background subtraction algorithm is used to get the binary mask of moving objects for segmenting the video sequence captured by fixed camera. Then a mixture of hidden Markov models (HMMs) using the expectation-maximization (EM) scheme is fitted to the motion data extracted from the binary mask. Unlike previous literatures using k-means where every observed data set only assigned to a single HMM, the proposed approach allows every video segment to belong to more than a single HMM with some probability. Experiments with real data demonstrate the benefit when there is more "overlap" in the processes generating the data. The promising potential of HMM-based motion clustering for mining similar video segments from surveillance video is also indicated by the experimental results