Multi-person tracking-by-detection with local particle filtering and global occlusion handling

This paper presents a detection-based method for tracking an uncertain number of persons in complex scenarios with frequent occlusions. Frame-by-frame data association based particle filters are adopted to track targets in occlusion-free regions. When occlusion is detected, the associated trackers a...

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Bibliographic Details
Main Authors: Yaowen Guan, Xiaoou Chen, Deshun Yang, Yuqian Wu
Format: Conference Proceeding
Language:English
Subjects:
Detectors
Frequency modulation
Image color analysis
multi-object tracking
occlusion handling
particle filtering
Prediction algorithms
surveillance
Target tracking
Tracking-by-detection
Trajectory
Online Access:Request full text
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Summary:This paper presents a detection-based method for tracking an uncertain number of persons in complex scenarios with frequent occlusions. Frame-by-frame data association based particle filters are adopted to track targets in occlusion-free regions. When occlusion is detected, the associated trackers are deactivated and they are re-activated when the tracked persons are re-identified after occlusion. The re-identification problem is solved by global data association. And the association cost matrix only integrates information collected from the frames after occlusion to avoid tracking failure caused by false detections during occlusion. Furthermore, we improve the particle initialization by motion prediction and automatically configured dynamic model. Experimental results show that the proposed algorithm effectively reduces id switches and lost trajectories which happen frequently in local filtering methods. In the meantime, the algorithm is suitable for time-critical applications.
ISSN:1945-7871
1945-788X
DOI:10.1109/ICME.2014.6890149