iccv iccv2013 iccv2013-86 iccv2013-86-reference knowledge-graph by maker-knowledge-mining
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Author: Ping Wei, Nanning Zheng, Yibiao Zhao, Song-Chun Zhu
Abstract: Action recognition has often been posed as a classification problem, which assumes that a video sequence only have one action class label and different actions are independent. However, a single human body can perform multiple concurrent actions at the same time, and different actions interact with each other. This paper proposes a concurrent action detection model where the action detection is formulated as a structural prediction problem. In this model, an interval in a video sequence can be described by multiple action labels. An detected action interval is determined both by the unary local detector and the relations with other actions. We use a wavelet feature to represent the action sequence, and design a composite temporal logic descriptor to describe the action relations. The model parameters are trained by structural SVM learning. Given a long video sequence, a sequential decision window search algorithm is designed to detect the actions. Experiments on our new collected concurrent action dataset demonstrate the strength of our method.
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