Infrared target tracking based on proximal robust principal component analysis method

Abstract

Infrared target tracking plays an important role in both civil and military fields. The main challenges in designing a robust and high-precision tracker for infrared sequences include overlap, occlusion, and appearance change. To this end, this paper proposes an infrared target tracker based on the proximal robust principal component analysis method. Firstly, the observation matrix is decomposed into a sparse occlusion matrix and a low-rank target matrix, and the constraint optimization is carried out with an approaching proximal norm which is better than l1-norm. Then, the Alternating Direction Method of Multipliers (ADMM) is employed to solve this convex optimization problem by estimating the variables alternately. Finally, the framework of particle filter with model update strategy is exploited to locate the target. Through a series of experiments on real infrared target sequences, the effectiveness and robustness of our algorithm are proved.