关键词: 目标跟踪, 核相关滤波, 多特征互补, 多核学习

Abstract:

Aiming at the problem that the Kernel Correlation Filtering（KCF） tracking algorithm has poor positioning performance and stability in complex environments, an enhanced multi-core correlation filtering tracking algorithm for fast scale estimation is proposed. The algorithm is aimed at the problem that the kernel correlation filtering algorithm can not adapt to the target scale change in the tracking process. The fast discriminant scale estimation is transplanted to the kernel correlation filter tracking framework, and the target scale problem of the tracking process is solved. A multi-core correlation filter with multiple features is proposed for the problem that the single-core correlation filter of single feature has poor tracking adaptability in complex environment. The filter can describe different information by using KCF multi-channel characteristics and different features. It adopts linear combination of multiple identical cores, each core corresponds to one feature, and combines fast scale estimation to improve the tracking performance while ensuring the real-time performance of the algorithm. By conducting experiments on the OTB2013 target tracking dataset and comparing with the algorithms with excellent performance in recent years, the results show that compared with the traditional KCF algorithm using HOG features, the accuracy is improved by 10.9% and the success rate is increased by 16.2%. Compared with the CN2 algorithm using the CN feature, the accuracy is improved by 20.6% and the success rate is increased by 19.6%. The experimental results show that the proposed algorithm is better than the other related filtering algorithms in the target scale change and complex environment, which proves the effectiveness and robustness of the proposed algorithm.

Key words: object tracking, kernelized correlation filter, multiple feature complementation, multi-kernel learning