关键词: 核方法, 判别分类器, 傅里叶变换, TSK模糊逻辑系统, 多核融合

Abstract:

For the problem how to accurately and quickly locate targets in the current target tracking field, the core content of most popular trackers is to combine a kernel method to train a discriminative classifier to distinguish the target from the surrounding environment. For example, the kernel correlation filter algorithm (KCF) combines the Fourier transform with the kernel discriminant classifier to improve the speed of tracking, and the improved fuzzy kernel correlation filter (FKCF) algorithm introduced by Takagi-Sugeno-Kang fuzzy logic system (TSK-FLS) is used to improve the accuracy of tracking. Some improved KCF-based algorithms have proposed solutions to partial tracking problems. However, existing algorithms still have some room for improvement in improving accuracy. Aiming at this deficiency, based on FKCF, a new multi-fuzzy kernels correlation filter (MFKCF) is derived using multi-kernel fusion. MFKCF inherits the characteristics of high speed of KCF and high accuracy of FKCF and blurs the polynomial kernel and the Gaussian kernel. And it combines the fuzzified kernel function as a new target kernel function. Due to the above two improvements, the proposed algorithm is better than KCF and FKCF in the accuracy of tracking. KCF, FKCF and MFKCF are carried out on 30 randomly selected videos on 4 databases such as OTB50. The experimental results show that MFKCF performs well on the whole, accuracies of the MFKCF on 10 types of common attributes are all improved.

Key words: kernel method, discriminant classifier, Fourier transform, TSK fuzzy logic system, multi-kernel fusion