A Novel Moving Object Tracking Method Using ICA-R
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Combined feature evaluation for adaptive visual object tracking
Computer Vision and Image Understanding
Depth assisted occlusion handling in video object tracking
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Objects detection and tracking in highly congested traffic using compressed video sequences
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
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We propose a complete solution to robust and accurate object tracking in face of various types of occlusions which pose many challenges to correct judgment of occlusion situation and proper update of target template. In order to tackle those challenges, we first propose a content-adaptive progressive occlusion analysis (CAPOA) algorithm. By combining the information provided by spatiotemporal context, reference target, and motion constraints together, the algorithm makes a clear distinction between the target and outliers. Accurate tracking of an occluded target is achieved by rectifying the target location using the variant-mask template matching (VMTM). In order to deal with template drift in the process of template update, we propose a drift-inhibitive masked Kalman appearance filter (DIMKAF) which accurately evaluates the influence of template drift when updating the masked template. Finally, we devise a local best match authentication (LBMA) algorithm to handle complete occlusions, so that we can achieve a much more trustworthy detection of the end of an arbitrarily long complete occlusion. Experimental results show that our proposed solution tracks targets reliably and accurately no matter when they are under: short-term, long-term, partial or complete occlusions.