Real-time infrared target tracking based on ℓ₁minimization and compressive features

Tracking a target in infrared (IR) sequences is a challenging task because of low resolution, low signal-to-noise ratios, occlusion, and poor target visibility. For many civil and military applications, the realtime requirement is always a key factor for tracking algorithms to be used. This undoubtedly makes tracking in IR sequences more difficult. This paper presents a real-time IR target tracking under complex conditions based on ℓ₁minimization and compressive features. First, we adopt a sparse measurement matrix to project the high-dimensional Harr-like features to low-dimensional features that are applied to the appearance modeling. This appearance model allows significant reduction in the computational cost of the target-tracking phase. Then, the appearance model is introduced into the framework of the popular ℓ₁tracker. Each IR target candidate is represented by the appearance template based on the structure of sparse representation. Finally, the candidate that has the minimum reconstruction error is selected as the tracking result. The proposed tracking method can combine the real-time advantages of the compressive tracking and the robustness of the ℓ₁tracker. Experimental results on challenging IR image sequences including both aerial targets and ground targets show that the proposed algorithm has better robustness and real-time performance in comparison with two state-of-the-art tracking algorithms.