Infrared target tracking based on correlation filter with multi-features fusion

An infrared target tracking algorithm with multi-features was proposed in consideration of low resolution, poor contrast, low signal-to-noise ratio and lack of texture information of infrared target. The background perceptual correlation filter was used to generate a large number of real samples, and the HOG feature and motion feature were extracted for the infrared target. The feature fusion was performed by linear interpolation, and the advantages of the respective feature were well utilized to achieve accurate tracking of the infrared target motion. In addition, it was proposed to adopt the spatial weighting window instead of the cosine window in the traditional correlation filter to highlight the center position of the target and suppressed the edge effect. The VOT-TIR 2016 dataset was utilized to evaluate algorithm performance in comparison it with 15 popular algorithms. Simulation results show that the algorithm's scores on accuracy and success rate are 0.751 and 0.697 respectively. Furthermore, it is 8.8% and 15.4% higher than the second-ranking algorithm, which shows that the proposed algorithm has certain research value.