  Most of the correlation filter based tracking algorithms can achieve good
performance and maintain fast computational speed. However, in some complicated
tracking scenes, there is a fatal defect that causes the object to be located
inaccurately. In order to address this problem, we propose a particle filter
redetection based tracking approach for accurate object localization. During
the tracking process, the kernelized correlation filter (KCF) based tracker
locates the object by relying on the maximum response value of the response
map; when the response map becomes ambiguous, the KCF tracking result becomes
unreliable. Our method can provide more candidates by particle resampling to
detect the object accordingly. Additionally, we give a new object scale
evaluation mechanism, which merely considers the differences between the
maximum response values in consecutive frames. Extensive experiments on OTB2013
and OTB2015 datasets demonstrate that the proposed tracker performs favorably
in relation to the state-of-the-art methods.
