An improved KCF tracking algorithm based on multi-feature and multi-scale

The purpose of visual tracking is to associate the target object in a continuous video frame. In recent years, the method based on the kernel correlation filter has become the research hotspot. However, the algorithm still has some problems such as video capture equipment fast jitter, tracking scale transformation. In order to improve the ability of scale transformation and feature description, this paper has carried an innovative algorithm based on the multi feature fusion and multi-scale transform. The experimental results show that our method solves the problem that the target model update when is blocked or its scale transforms. The accuracy of the evaluation (OPE) is 77.0%, 75.4% and the success rate is 69.7%, 66.4% on the VOT and OTB datasets. Compared with the optimal one of the existing target-based tracking algorithms, the accuracy of the algorithm is improved by 6.7% and 6.3% respectively. The success rates are improved by 13.7% and 14.2% respectively.