Multi-feature local sparse representation for infrared pedestrian tracking

Abstract

Robust tracking of infrared (IR) pedestrian targets with various backgrounds, e.g.appearance changes, illumination variations, and background disturbances, is a great challenge in the infrared image processing field.In the paper, we address a new tracking method for IR pedestrian targets via multi-feature local sparse representation (SR), which consists of three important modules.In the first module, a multi-feature local SR model is constructed.Considering the characterization of infrared pedestrian targets, the gray and edge features are first extracted from all target templates, and then fused into the model learning process.In the second module, an effective tracker is proposed via the learned model.To improve the computational efficiency, a sliding window mechanism with multiple scales is first used to scan the current frame to sample the target candidates.Then, the candidates are recognized via sparse reconstruction residual analysis.In the third module, an adaptive dictionary update approach is designed to further improve the tracking performance.The results demonstrate that our method outperforms several classical methods for infrared pedestrian tracking.