Adaptive feature-spatial representation for Mean-shift tracker

Mean-shift tracker plays an important role in computer vision applications due to its efficiency in mode seeking. By encoding the spatial information appropriately, the robustness of tracking could be greatly enhanced. However, to account for the deformation and other sources of variation of the tracking object, the spatial configuration should not be fixed apriori and it is more suitable to be adapted online. To this end, this paper presents a novel method to formulate an adaptive feature-spatial representation (FSR) for mean-shift tracking. By encoding blocking features of the tracking object with a set of adaptively weighted and spatially distributed tunable kernels, the object variations, like deformations and partial occlusions, can be handled appropriately. Extensive experiments under various conditions clearly demonstrate the obvious advantage of our approach compared to the classical mean-shift trackers.