In order to solve the inaccurate detecting problem in the long-time target tracking process, an AdaBoost multi-target detection algorithm is proposed based on superpixel segmentation and mixed weight. In the dynamic model, the mixed weight of AdaBoost algorithm is calculated out, the moving targets are detected, and the search area is determined, so as to improve the multi-target tracking and detecting capabilities. At the training stage, the superpixel segment is formed by using the SLIC segmentation and the Mean-Shift clustering, and the appearance model of the targets is built. At the tracking stage, the histogram and the confidence map of the template are created by using the superpixel feature pool, and the observation model and the motion model are built. The maximum posterior estimation is computed by using the particle filter and Bayes model, so as to realize the detection of sheltered moving targets. Experimental results show that: The proposed algorithm can effectively deal with the problems of varying-number, multi-target detecting and sheltered target detecting, and improve the real-time performance of the detection.