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Visual object tracking based on particle filter re-detection

The accurate localization of a object target is a challenging research issue in visual tracking. Most correlation filter based tracking algorithms has been degraded their performances because of the weaknesses of their search strategy. This paper investigates the problem of accurate location the object target in visual tracking sequences. We propose a novel particle filter re-detection tracking approach for target re-location, when the kernelized correlation filters tracking result becomes unreliable. Additionally, we give a new target scale evaluation. Different from other proposed scale search strategies, our method merely consider the difference between the maximum value of the response map of adjacent frames. Extensive experiments are performed on the OTB2013 dataset. On the result of this benchmark, the proposed approach achieves a pretty performance.