A hierarchical estimator for object tracking

Authors:
Chin-Wen Wu;Yi-Nung Chung;Pau-Choo Chung
Affiliations:
Department of Electrical Engineering, Institute of Computer and Communication Engineering, National Cheng Kung University, Tainan, Taiwan;Department of Electrical Engineering, National Changhua University of Education, Changhua, Taiwan;Department of Electrical Engineering, Institute of Computer and Communication Engineering, National Cheng Kung University, Tainan, Taiwan
Venue:
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
Year:
2010

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Abstract

A closed-loop local-global integrated hierarchical estimator (CLGIHE) approach for object tracking using multiple cameras is proposed. The Kalman filter is used in both the local and global estimates. In contrast to existing approaches where the local and global estimations are performed independently, the proposed approach combines local and global estimates into one for mutual compensation. Consequently, the Kalman-filter-based data fusion optimally adjusts the fusion gain based on environment conditions derived from each local estimator. The global estimation outputs are included in the local estimation process. Closedloop mutual compensation between the local and global estimations is thus achieved to obtain higher tracking accuracy. A set of image sequences frommultiple views are applied to evaluate performance. Computer simulation and experimental results indicate that the proposed approach successfully tracks objects.