Resolving occlusion and segmentation errors in multiple video object tracking

Hsu Yung Cheng, Jenq Neng Hwang

研究成果: 雜誌貢獻會議論文同行評審

2 引文 斯高帕斯(Scopus)


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.

期刊Proceedings of SPIE - The International Society for Optical Engineering
出版狀態已出版 - 2009
事件Computational Imaging VII - San Jose, CA, United States
持續時間: 19 1月 200920 1月 2009


深入研究「Resolving occlusion and segmentation errors in multiple video object tracking」主題。共同形成了獨特的指紋。