Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift Analysis and Applications
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Probabilistic tracking in joint feature-spatial spaces
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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We present a novel mean shift approach in this paper for robust object tracking based on an inertial potential model. Conventional mean shift based trackers exploit only appearance information of observation to determine the target location which usually cannot effectively distinguish the foreground from background in complex scenes. In contrast, by constructing the inertial potential model, the proposed algorithm makes good use of motion information of previous frames adaptively to track the target. Then the probability of all candidates is modeled by considering both the photometric and motion cues in a Bayesian manner, leading the mean shift vector finally converge to the location with maximum likelihood of being the target. Experimental results on several challenging video sequences have verified that the proposed method is compared very robust and effective with the traditional mean shift based trackers in many complicated scenes.