Trajectory parsing by cluster sampling in spatio-temporal graph

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 STGraph 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 motion primitive is a graph vertex and has six bonds connecting to neighboring vertices. Based on the ST-Graph, we jointly solve three tasks: 1)spatial segmentation; 2)temporal correspondence and 3)object recognition, by flipping the labels of the motion primitives. We also adapt the scene geometric and statistical information as strong prior. Then the inference computation is formulated in a Markov Chain and solved by an efficient cluster sampling. We apply the proposed approach to various cha...

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Xiaobai Liu, Liang Lin, Song Chun Zhu, Hai Jin
• Keywords: Cluster Sampling, Cluster Sampling Algorithm, Computer Vision, CVPR 2009, Motion Primitives