Mean Shift 是一種密度梯度的無參數(shù)估計方法，應用于目標跟蹤領域有較好的性能。
然而Mean Shift 算法是一種半自動跟蹤方法。為此，提出邊緣檢測與Mean Shift 相結合的方法。利用結合小波的Canny 邊緣檢測方法對初始幀進行目標檢測，通過力矩在圖像處理中的計算方法得出目標的形心位置，為Mean Shift 方法提供初始信息。在目標丟失時，可以利用形心方法修正Mean Shift 的迭代跟蹤過程。實驗結果表明，本方法可以實現(xiàn)對目標的自動跟蹤，同時有效修正了對紅外目標的跟蹤偏移。
關鍵詞:紅外目標；形心定位；自動跟蹤；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