英文摘要 | Action recognition is one of the most active research ¯elds in computer vision. Despite the increasing amount of work done in this ¯eld in recent years, action recognition remains a challenging task for the several reasons, such as the action feature, the high degree of freedom of human body, nuisance factors, etc... The main contributions of our work are summarized as follows: 1, we present a comprehensive survey of works in the past couple of decades to address the problems of representation, recognition and learning of human activities from video and related applications. 2, In this chapter, we proposed a novel method for classifying human actions in a series of image sequences containing certain actions. Human action in image sequences can be recognized by a time-varying contour of human body. We first extracted shape context of each contour to form the feature space. Then the dominant sets approach is used for feature clustering and classification to obtain the labeled sequences. Finally, we used a smoothing algorithm upon the labeled sequences to recognize human actions. The proposed dominant sets-based approach has been tested in comparison to three classical methods: K-means, mean shift, and Fuzzy-Cmean. Experimental results demonstrate that the dominant sets-based approach achieves the best recognition performance. Moreover, our method is robust to non-rigid deformations, significant scale changes, high action irregularities, and low quality video. 3, Group action recognition is a challenging task in computer vision due to the large complexity induced by multiple motion patterns. This paper aims at analyzing group actions in video clips containing several activities. We combine the probability summation framework with the space-time (ST) interest points for this task. First, ST interest points are extracted from video clips to form the feature space. Then we use k-means for feature clustering and build a compact representation, which is then used for group action classification. The proposed approach has been applied to classification tasks including four classes: badminton, tennis, basketball, and soccer videos. The experimental results demonstrate the advantages of the proposed approach. 4, In this chapter, we proposed a method using sparse representation to compress the visual codebook. We first represent the training data using sparse representation of the old visual codebook, and then learn the weight of every word that is used ... |
修改评论