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Nonparametric discovery of activity patterns from video collections
2012
2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
We propose a nonparametric framework based on the beta process for discovering temporal patterns within a heterogenous video collection. Starting from quantized local motion descriptors, we describe the long-range temporal dynamics of each video via transitions between a set of dynamical behaviors. Bayesian nonparametric statistical methods allow the number of such behaviors and the subset exhibited by each video to be learned without supervision. We extend the earlier beta process HMM in two
doi:10.1109/cvprw.2012.6239170
dblp:conf/cvpr/HughesS12
fatcat:xg5bvpi3krelnbx7cgjztautv4