Improved target tracking algorithm based on kernelized correlation filter

Abstract

We propose an improved object tracking algorithm based on kernelized correlation filter (KCF), which can overcome the drawback of traditional KCF algorithms in that they cannot effectively adapt to target-scale variations and target occlusion in tracking. First, the target-scale pyramid is built, whose histogram of oriented gradients feature extracted of every layer multiplied by the correlation filters; the maximum response of current scale of the filters is the best target scale. In addition, the improved algorithm is combined with the improved correlation filter framework, and the background information around the target is appropriately increased. When the target is occluded, the background information can be effectively used to track the target. The proposed algorithm is validated on the benchmark evaluation and compared with the traditional algorithms, such as KCF and circulation structure of tracking-by-detection with kernel. The results indicate that our tracking accuracy can reach 66.9% and the success rate can be 58.2. When the target is scale variation, the accuracy and success rate increase by 1.1% and 10.3%, respectively, compared with KCF. If the tracked target is occluded, a second improved algorithm is compared with the detection algorithm which only adds the scale detection, the tracking accuracy increases by 8%, and the success rate increases by 4.9%.