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

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 vocabu...

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Bibliographic Details
Published in:Advances in multimedia 2015-01, Vol.2015 (2015), p.1-10
Main Authors: Huang, Shao-nian, Khuhro, Mansoor Ahmed, Huang, Dong-jun
Format: Article
Language:English
Subjects:
Agglomeration
Birthdays
Channels
Content analysis
Learning
Methods
Multimedia
Representations
Surveillance
Temporal logic
Visual
Vocabularies & taxonomies
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Summary: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.
ISSN:1687-5680
1687-5699
DOI:10.1155/2015/698316