Adaptive Correlation Filtering Algorithm Suitable for Long-Term Tracking

In view of the problem that fast motion,occlusion and other complex conditions can easily cause template drift and the track failure in long-term tracking,an adaptive correlation filtering algorithm suitable for the long time tracking is proposed.First of all,this paper integrates HOG feature,CN feature and gray feature to enhance the discriminant power of features.While it combines EdgeBoxes to generate detection proposals to find the optimal proposal to realize the adaptive scale and aspect ratio,this paper uses high confidence tracking results to avoid the template being destroyed.The target’s speed and the number of edge groups are combined to form a new adaptive update rate,and this paper corrects the scale of the target box for each frame.Finally,in the case of trace failure,the incremental learning detector is applied to restore the target position by sliding window.Compared with other 7 algorithms based on correlation filtering on the standard test set,the experimental results show that the proposed algorithm achieves better results in accuracy and success rate.