基于Mean Shift的红外目标自动跟踪方法

Mean Shift 是一种密度梯度的无参数估计方法，应用于目标跟踪领域有较好的性能。
然而Mean Shift 算法是一种半自动跟踪方法。为此，提出边缘检测与Mean Shift 相结合的方法。利用结合小波的Canny 边缘检测方法对初始帧进行目标检测，通过力矩在图像处理中的计算方法得出目标的形心位置，为Mean Shift 方法提供初始信息。在目标丢失时，可以利用形心方法修正Mean Shift 的迭代跟踪过程。实验结果表明，本方法可以实现对目标的自动跟踪，同时有效修正了对红外目标的跟踪偏移。
关键词:红外目标；形心定位；自动跟踪；Mean Shift

Infrared target auto-tracking method based on Mean Shift algorithm Lian Jie, Han Chuanjiu (Dept. of Information and Communication, Guilin Univ. of Electronic Technology, Guilin 541004) Abstract: Mean Shift is a nonparametric estimator of density gradient. Aside from the good application result in target tracking, it is a semiautomatic tracking algorithm. Therefore, a
combination method of Mean Shift and edge detection is proposed. Canny edge detection based on wavelet is used to detect target in the first frame. Through moment knowledge used in image processing, the target center is computed for Mean Shift to initialize. When occlusion makes the target lose, that can modify Mean Shift tracking. Experimental results show that the proposed algorithm can track automatically and correct the excursion during tracking.
Key words: infrared target；center locating；auto-tracking；Mean Shift