Tracking non-stationary appearances and dynamic feature selection

• Published in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 2; pp. 1059 - 1066 vol. 2
• Main Authors: Yang, M., Wu, Y.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Adaptation model; Closed-form solution; Iterative algorithms; Learning systems; Lighting; Target tracking; Video surveillance
• ISBN: 0769523722; 9780769523729
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPR.2005.352

Since the appearance changes of the target jeopardize visual measurements and often lead to tracking failure in practice, trackers need to be adaptive to non-stationary appearances or to dynamically select features to track. However, this idea is threatened by the risk of adaptation drift that roots in its ill-posed nature, unless good constraints are imposed. Different from most existing adaptation schemes, we enforce three novel constraints for the optimal adaptation: (1) negative data, (2) bottom-up pair-wise data constraints, and (3) adaptation dynamics. Substantializing the general adaptation problem as a subspace adaptation problem, this paper gives a closed-form solution as well as a practical iterative algorithm. Extensive experiments have shown that the proposed approach can largely alleviate adaptation drift and achieve better tracking results.