Tracking of Abrupt Motion Using Wang-Landau Monte Carlo Estimation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Tracking nonstationary visual appearances by data-driven adaptation
IEEE Transactions on Image Processing
Semi-supervised particle filter for visual tracking
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Discriminative tracking by metric learning
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Hierarchical Kalman-particle filter with adaptation to motion changes for object tracking
Computer Vision and Image Understanding
Co-trained generative and discriminative trackers with cascade particle filter
Computer Vision and Image Understanding
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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.