Title
Integrated video object tracking with applications in trajectory-based event detection
Abstract
This work presents an automated and integrated framework that robustly tracks multiple targets for video-based event detection applications. Integrating the advantages of adaptive particle sampling and mathematical tractability of Kalman filtering, the proposed tracking system achieves both high tracking accuracy and computational simplicity. Occlusion and segmentation error cases are analyzed and resolved by constructing measurement candidates via adaptive particle sampling and an enhanced version of probabilistic data association. Also, we integrate the initial occlusion handling module in the tracking system to backtrack and correct the object trajectories. The reliable tracking results can serve as the foundation for automatic event detection. We also demonstrate event detection by classifying the trajectories of the tracked objects from both traffic monitoring and human surveillance applications. The experimental results have shown that the proposed tracking mechanism can solve the occlusion and segmentation error problems effectively and the events can be detected with high accuracy.
Year
DOI
Venue
2011
10.1016/j.jvcir.2011.07.001
J. Visual Communication and Image Representation
Keywords
Field
DocType
high tracking accuracy,integrated video,event detection,trajectory-based event detection,reliable tracking result,tracking system,proposed tracking mechanism,adaptive particle sampling,video-based event detection application,proposed tracking system,initial occlusion handling module,automatic event detection,kalman filter,tracking
Computer vision,Pattern recognition,Segmentation,Computer science,Tracking system,Kalman filter,Video tracking,Data association,Artificial intelligence,Sampling (statistics),Probabilistic logic,Trajectory
Journal
Volume
Issue
ISSN
22
7
1047-3203
Citations 
PageRank 
References 
21
0.92
21
Authors
2
Name
Order
Citations
PageRank
Hsu-Yung Cheng124323.56
Jenq-Neng Hwang21675206.57