A scale-adaptive real-time target tracking algorithm based on KCF

Abstract

Kernelized Correlation Filter (KCF) algorithm has been successfully applied in object tracking. By finding the maximum confidence in the search region, the candidate target block is determined by KCF with constant size. When the scale of the target changes during its actual movement, the traditional KCF algorithms often fail to track the target. Moreover, it is difficult to judge whether the object is missing due to the lack of self-adaptive threshold regulation scheme in the KCF tracking. In order to solve these problems, this paper proposes a scale-adaptive target tracking algorithm based on KCF, which is mainly divided into the following steps. Firstly, the positive and negative samples of the nearest neighbor classifier are initialized by the selected target and its surrounding non-target areas. Secondly, the peak point of the spectral response of the current frame image is obtained by executing the KCF algorithm, which is the target center point. Thirdly, the scale change of object is obtained by calculating the ratio of the bandwidth of spectral response peak centered candidate regions and the target in the previous frame. Fourthly, the scaled candidate target is confirmed by calculating the sample similarity between it and the Nearest Neighbor Classifier (NNC). Finally, the positive and negative samples of the nearest neighbor classifier are updated with the confirmed tracking target and non-target respectively. Extensive experiments have been carried on four test video sequences. The experimental results show that our proposed method achieves a higher success rate and accuracy with less running time compared with the state-of-the- art methods.