| Summary: | For the problem how to accurately and quickly distinguish the target from the background in the current target tracking field, the core task of most trackers is how to train a discriminant classifier to distinguish between the target and the surrounding environment. At present, the more advanced kernel correlation filter algorithm (KCF) and improved discriminant correlation filter (DCF) can combine the discriminant classifier with the Fourier transform to improve the tracking speed. Some KCF-based optimization algorithms provide solutions to partial tracking problems, such as the KCF algorithm for scale problems and the KCF algorithm for target disappearance. However, existing algorithms still have some room for improvement in improving accuracy. Aiming at this deficiency, a new fuzzy kernel correlation filter (FKCF) is derived using Takagi-Sugeno-Kang fuzzy logic system (TSK-FLS) based on kernel correlation filter. FKCF inherits the characteristics of high speed and small computational complexity of KFC, and further to improve the robustness, replacing the previous simple Gaussian mapping with the fuzzy membership function and introducing the consequent parameters of TSK-FLS in the process of kernel calculation. Thus, FKCF achieves better tracking accuracy than traditional KCF. Extensive experiments are carried out on 50 randomly selected videos on 4 databases such as OTB50. The experimental results show that accuracies of the FKCF on 10 types of common attributes are all improved compared with the traditional KCF.
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