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Aiming at the disadvantages of the existing human action recognition and positioning methods, an improved human action recognition and positioning algorithm is proposed. First, hierarchical segmentation is applied on each video frame to get a set of segment trees, each of which is considered as a candidate segment tree of the human body. Second, we prune the candidates by exploring several cues such as shape, articulated objects’ structure and global foreground color. Finally, we track each segment of the remaining segment trees in time both forward and backward. The experimental results show that, the performance of our method is better than the state-of-art action recognition methods on two challenging benchmark datasets UCF-Sports and HighFive, and at the same time produce good action localization results.