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Advances in Multimedia
Volume 2015, Article ID 698316, 10 pages
Research Article

High-Level Codewords Based on Granger Causality for Video Event Detection

1School of Information Science and Engineering, Central South University, Changsha 410083, China
2School of Computer and Information Engineering, Hunan University of Commerce, Changsha 410205, China

Received 18 January 2015; Revised 19 May 2015; Accepted 7 June 2015

Academic Editor: Luigi Atzori

Copyright © 2015 Shao-nian Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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