Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Integral Histogram: A Fast Way To Extract Histograms in Cartesian Spaces
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets
IEEE Transactions on Pattern Analysis and Machine Intelligence
An integrated Monte Carlo data association framework for multi-object tracking
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Vision-Based Preceding Vehicle Detection and Tracking
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
IEEE Transactions on Multimedia
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Tracking appearance similar objects is very challenging. Conventional approaches often encounter "hijack" problem. That is to say, the tracking results for the smaller objects will be attracted to the larger one in the close vicinity. In this paper, we propose a decentralized particle filter approach for similar objects tracking. When the objects are close, the tracking results for the larger one will be masked and its influence will be eliminated. In principle, the tracker for the smaller object needs to be run two times, which increase the time costs. To tackle this, we construct the integral image for the mask region and dramatically decrease the calculation time of the evaluation of likelihood functions in the masked image. Experimental results show that the proposed approach effectively avoids "hijack" problems.