Abstract：Focusing on the issue that the Kernelized Correlation Filter (KCF) can not respond to the target scale change, a KCF target tracking algorithm based on fast discriminative scale estimation was proposed. Firstly, the target position was estimated by KCF. Then, a fast discriminative scale filter was learned online by using a set of target samples with different scales. Finally, an accurate estimation of the target size was obtained by applying the learned scale filter at the target position. The experiments were conducted on Visual Tracker Benchmark video sequence sets, and comparison was performed with the KCF algorithm based on Discriminative Scale Space Tracking (DSST) and the traditional KCF algorithm. Experimental results show that the tracking accuracy of the proposed algorithm is 2.2% to 10.8% higher than that of two contrast algorithms when the target scale changes, and the average frame rate of the proposed algorithm is also 19.1% to 68.5% higher than that of KCF algorithm based on DSST. The proposed algorithm has strong adaptability and high real-time performance to target scale change.
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2019, Vol. 39 
