A new approach for long-term person tracking

This paper investigates long-term visual person tracking using particle filter as the underlying framework and online boosting as the detection strategy. In the case of the being tracked person with abrupt motion, under occlusion or in low sample rate of video source, two main issues rise inevitably...

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Bibliographic Details
Main Authors: Deqian Fu, Seong Tae Jhang
Format: Conference Proceeding
Language:English
Subjects:
Boosting
Computer vision
long-term
Observers
online boosting
particle filter
Particle filters
person tracking
Robustness
Tracking
Visualization
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Summary:This paper investigates long-term visual person tracking using particle filter as the underlying framework and online boosting as the detection strategy. In the case of the being tracked person with abrupt motion, under occlusion or in low sample rate of video source, two main issues rise inevitably. One is the poor constraint of person motion model, and the other is the drastic variation of pose or incomplete appearance when the person reappears. We address the problems with an integrated framework of multiple observers, and online boosting algorithm with independent features and its static and dynamic combination aiming to balance the tradeoff of adaption and drift.
DOI:10.1109/WCICA.2012.6359411