Spatiotemporal Sparsity Induced Similarity Measure for Human Action Recognition
An-An Liu, Dong Yeob Han · International Journal of Digital Content Technology and its Applications · 2010
In this paper, we propose a model-free method for human action recognition via sparse spatiotemporal representation. Similar to the template matching method, the philosophy of the proposed method is to decompose each video sample containing one kind of human actions as a 1 sparse linear combination of several video samples containing multiple kinds of human actions. To realize this goal, we mainly focus on three problems, feature point detection and description for human action, spatiotemporal feature construction for action video representation, and action video decomposition via sparse representation. The contributions of this method lies in three-folds: 1) the proposed method does not depend on complicated model selection and learning; 2) it can handle the action recognition under difference scenarios by multiple persons; 3) the generalization ability of the method can be easily extended by simply adding bases, the new labeled action video. To demonstrate the superiority of the proposed method, we evaluate it on KTH dataset, the well known dataset for human action recognition. Large scale experiment shows the accuracy and robustness of the method. Moreover, the proposed method outperforms most of the state-of-art methods for human behavior recognition.