Multi-Target Visual Tracking by Bayesian Filtering with Occlusion Handling on an Active Camera Platform
Date Issued
2006
Date
2006
Author(s)
Lai, Chuan-Wen
DOI
en-US
Abstract
In visual tracking, multi-target tracking (MTT) systems encounter the problem that unavoidably moving targets may occlude each other and the measurement process of each target becomes dependent. We construct a tracking system with considering joint image likelihood to recognize targets, even though the appearances of the target are identical. Also, the multiple hypotheses of the targets’ depth level are utilized for occlusion handling. In order to enhance system performance, we extend the sampling importance resampling (SIR) particle filter with the separated importance functions for tracking each target and detection. Furthermore, when targets occlude together, the state vector of these targets is transferred into a joint state vector, and the MCMC (Markov Chain Monte Carlo) based particle filter is then proposed for efficient sampling in the high-dimensional joint state during occlusion. Furthermore, a control strategy for the active camera is proposed in order to move the camera such that the surveillance area will contain the most information. The overall performance is validated in the experiments and shows the robustness with real-time tracking.
Subjects
多目標物追蹤
影像追蹤
馬可夫鏈蒙地卡羅
粒子濾波器
結合影像相似度
主動相機
Multi-Target Tracking
Visual Tracking
Markov Chain Monte Carlo
Particle Filter
Sequential Monte Carlo
Joint Image Likelihood
Active Camera
Type
thesis
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