The infrared moving target in complex background has the characteristics of low contrast and few details,and it is difficult to realize a stable and continuous tracking.After analyzing the characteristics of infrared moving target,an improved tracking algorithm based on sparse encoding and feature selection is proposed.Using Logistic regression model and the supervised learning of the positive and negative samples,the optimal weight vector was calculated.Then the original feature templates and particle samples were projected to this vector by using a diagonal matrix,which can reduce the effect of cluttered background on moving target tracking and reduce the calculated amount.The update of each frame is used to adapt the moving target maneuverability in template updating strategy.Experimental results show that this algorithm is effective for infrared moving target tracking compared with IVT algorithm and L1 algorithm.
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雍杨,王升哲,王兵学,陈咸志.采用稀疏特征选择的红外运动目标跟踪方法[J].激光与红外,2015,45(4):446~451 YONG Yang, WANG Sheng-zhe, WANG Bing-Xue, CHEN Xian-zhi. Infrared moving target tracking based on sparse feature selection[J]. LASER & INFRARED,2015,45(4):446~451