Video event detection is a challenging problem in many applications, such as video surveillance and video content analysis. In this\npaper,we propose a new framework to perceive high-level codewords by analyzing temporal relationship between different channels\nof video features.The low-level vocabulary words are firstly generated after different audio and visual feature extraction. A weighted\nundirected graph is constructed by exploring the Granger Causality between low-level words. Then, a greedy agglomerative graph partitioning\nmethod is used to discover low-level word groups which have similar temporal pattern. The high-level codebooks\nrepresentation is obtained by quantification of low-level words groups. Finally, multiple kernel learning, combined with our high level\ncodewords, is used to detect the video event. Extensive experimental results show that the proposed method achieves preferable\nresults in video event detection.
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