Robust mean shift tracking with background information

Authors:
Zhao Liu;Guiyu Feng;Dewen Hu
Affiliations:
Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China;Institute of Computing Technology, Beijing Jiaotong University, Beijing, China;Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China
Venue:
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Year:
2012

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Abstract

The background-weighted histogram (BWH) has been proposed in mean shift tracking algorithm to reduce the interference of background in target localization. However, the BWH also reduces the weight for part of complex object. Mean shift with BWH model is unable to track object with scale change. In this paper, we integrate an object/background likelihood model into the mean shift tracking algorithm. Experiments on both synthetic and real world video sequences demonstrate that the proposed method could effectively estimate the scale and orientation changes of the target. The proposed method can still robustly track the object when the target is not well initialized.