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Trajectory parsing by cluster sampling in spatio-temporal graph
2009
2009 IEEE Conference on Computer Vision and Pattern Recognition
The objective of this paper is to parse object trajectories in surveillance video against occlusion, interruption, and background clutter. We present a spatio-temporal graph (ST-Graph) representation and a cluster sampling algorithm via deferred inference. An object trajectory in the ST-Graph is represented by a bundle of "motion primitives", each of which consists of a small number of matched features (interesting patches) generated by adaptive feature pursuit and a tracking process. Each
doi:10.1109/cvprw.2009.5206688
fatcat:mrj2ls6tarh4bplhexao5u62vu