Resolving occlusion and segmentation errors in multiple video object tracking

Hsu Yung Cheng, Jenq Neng Hwang

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


In this work, we propose a method to integrate the Kalman filter and adaptive particle sampling for multiple video object tracking. The proposed framework is able to detect occlusion and segmentation error cases and perform adaptive particle sampling for accurate measurement selection. Compared with traditional particle filter based tracking methods, the proposed method generates particles only when necessary. With the concept of adaptive particle sampling, we can avoid degeneracy problem because the sampling position and range are dynamically determined by parameters that are updated by Kalman filters. There is no need to spend time on processing particles with very small weights. The adaptive appearance for the occluded object refers to the prediction results of Kalman filters to determine the region that should be updated and avoids the problem of using inadequate information to update the appearance under occlusion cases. The experimental results have shown that a small number of particles are sufficient to achieve high positioning and scaling accuracy. Also, the employment of adaptive appearance substantially improves the positioning and scaling accuracy on the tracking results.

Original languageEnglish
Article number72460J
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2009
EventComputational Imaging VII - San Jose, CA, United States
Duration: 19 Jan 200920 Jan 2009


  • Adaptive particle sampling
  • Kalman filter
  • Video object tracking


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