Moving targets tracking on a mobile infrared platform and its real-time application on GPU

In the vision from a mobile infrared platform, the background scene keeps changing due to the moving camera, which results in more background clutters and noise. In this condition, it is difficult to track the target depending on brightness or gradient information. In this paper, we present a new tracking algorithm which segments the moving target according to the difference of velocity field between the target and background. In the proposed algorithm, the phase correlation method is introduced to transform two adjacent frames into one coordinate to remain a relatively static background. And then the Horn-Schunck optical flow is calculated in the tracking window to estimate the velocity field of the target. Finally we introduce the particle filter algorithm to estimate the location of the target, which has a more robust performance by optimizing the transition probability of particles with features of optical flow. Our algorithm can be organized in parallel processing mode enabled for GPU (graphics processing unit). By parallel computing, our algorithm is computationally efficient that it can work in real time. Experimental results show that the proposed algorithm can track the infrared target in real time on a moving platform.