Fast communication: Adaptive particle sampling and adaptive appearance for multiple video object tracking

Authors:
Hsu-Yung Cheng;Jenq-Neng Hwang
Affiliations:
Department of Computer Science and Information Engineering, National Central University, Chung-Li, Taiwan;Department of Electrical Engineering, University of Washington, Seattle, USA
Venue:
Signal Processing
Year:
2009

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Abstract

In this work, we propose an innovative method to integrate the Kalman filter and adaptive particle sampling for multiple video object tracking. Taking advantage of both the closed-form equations for optimal prediction and update from Kalman filters and the versatility of particle sampling for measurement selection under occlusion or segmentation error cases, the proposed method achieves both high tracking accuracy and computational simplicity. The adaptive particle sampling, which uses parameters updated by Kalman filters, can thus require only a small number of particles to achieve high positioning and scaling accuracy. Also, the concept of adaptive appearance is applied to enhance the robustness of occlusion handling. The experimental results confirm the effectiveness of the proposed method.