Novel tracking algorithm based on QP_TR method for low S/N ratio image sequences
基于QP_TR信任域方法的低信噪比序列图像目标跟踪*

Two problems exist in a tracking system for low S/N ratio image sequences on moving platforms. The global shift introduced by ego motion often makes targets fall outside the search region. The noise in the images can distract the tracker, This paper proposed a new QPTR based tracking algorithm, which solved the above-mentioned problems by the combination of Kalman filtration. Image stabilization and template matching were done with QPTR, while the noise was eliminated by targot state estimation. Compared to the TSS, the new method had better performance in that it enlarges the search region and reduces the number of template matching operation at the same time, Experiments on real image sequences show that the proposed method can stabilize the sequences very well and track targets steadily despite of the ego motion and noise.