Event Based Surveillance Video Synopsis Using Trajectory Kinematics Descriptors

  • Author(s):

Wei-Cheng Wang, Pau-Choo Chung Chun-Rong Huang and Wei-Yun Huang


    Video synopsis has been shown its promising perfor-mance in visual surveillance, but the rearranged fore-ground objects may disorderly occlude to each other which makes end users hard to identify the targets. In this paper, a novel event based video synopsis method is proposed by using the clustering results of trajectories of foreground objects. To represent the kinematic events of each trajectory, trajectory kinematics descriptors are applied. Then, affinity propagation is used to cluster trajectories with similar kinematic events. Finally, each kinematic event group is used to generate an event based synopsis video. As shown in the experiments, the generated event based synopsis videos can effectively and efficiently reduce the lengths of the surveillance videos and are much clear for browsing compared to the states-of-the-art video synopsis methods. Here is our dataset.


  • If you use the dataset, please cite our papers in the publication section.

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[1] Wei-Cheng Wang, Pau-Choo Chung, Chun-Rong Huang, Wei-Yun Huang, "Event Based Surveillance Video Synopsis Using Trajectory Kinematics Descriptors," in Proc. IAPR Conference on Machine Vision Applications, MVA’17, 2017.

[2] Chun-Rong Huang, Pau-Choo Chung, Di-Kai Yang, Hsing-Cheng Chen, and Guan-Jie Huang, "Maximum a Posteriori Probability Estimation for Online Surveillance Video Synopsis," IEEE Transactions on Circuits and Systems for Video Technology, vol. 24, no. 8, pp. 1417-1429, Aug. 2014.