Aerial infrared target tracking algorithm based on kernel correlation filtering and SIFT features in complex background

Abstract

Aerial infrared target tracking is one of the core technologies of the infrared imaging missile electro-optical countermeasures system and has important application value. However, in practical applications, the traditional kernel correlation filter (KCF) algorithm suffers from problems such as single scale and poor resistance to occlusion, resulting in poor tracking when the target changes scale or is in a complex background. In order to solve these problems, this paper proposes a way to improve the KCF algorithm. SIFT feature points are combined with correlation filtering methods to build a more flexible and adaptive feature representation, and a scale adaptivity mechanism is introduced to improve tracking performance. The paper is also validated by experiments based on infrared video datasets, and the results show that the improved KCF algorithm has better robustness and tracking performance in aerial infrared target tracking compared to the traditional KCF algorithm.