  The Discriminative Correlation Filter (CF) uses a circulant convolution
operation to provide several training samples for the design of a classifier
that can distinguish the target from the background. The filter design may be
interfered by objects close to the target during the tracking process,
resulting in tracking failure. This paper proposes an adaptive distraction
context aware tracking algorithm to solve this problem. In the response map
obtained for the previous frame by the CF algorithm, we adaptively find the
image blocks that are similar to the target and use them as negative samples.
This diminishes the influence of similar image blocks on the classifier in the
tracking process and its accuracy is improved. The tracking results on video
sequences show that the algorithm can cope with rapid changes such as occlusion
and rotation, and can adaptively use the distractive objects around the target
as negative samples to improve the accuracy of target tracking.
