Action Recognition Using Spatial-Temporal Context
The spatial-temporal local features and the bag of words representation have been widely used in the action recognition field. However, this framework usually neglects the internal spatial-temporal relations between video-words, resulting in ambiguity in action recognition task, especially for videos “in the wild”. In this paper, we solve this problem by utilizing the volumetric context around a video-word. Here, a local histogram of video-words distribution is calculated, which is referred as the “context” and further clustered into contextual words. To effectively use the contextual information, the descriptive video-phrases (ST-DVPs) and the descriptive video-cliques (ST-DVCs) are proposed. A general framework for ST-DVP and ST-DVC generation is described, and then action recognition can be done based on all these representations and their combinations. The proposed method is evaluated on two challenging human action datasets: the KTH dataset and the YouTube dataset. Experiment results confirm the validity of our approach.