Complex video event detection via pairwise fusion of trajectory and multi-label hypergraphs
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The video data is rich in motion event information. Detecting complex events and analyzing the inherent high-level semantics information have been a hot topic in video analysis and understanding. Detecting complex events in the video involves detecting multiple semantic concepts, describing features of multiple moving targets and discovering the relationship between low-level features and high-level semantic concepts. It can extract semantic concept patterns from various video features and original video data, thus bridging the semantic gap. Based on the hypergraph theory, this paper proposes to construct trajectory and multi-label hypergraphs considering the features of moving targets. The two hypergraphs are fused to detect complex events. The experimental results show that in comparison with other methods including ordinary graph based method and hypergraph based multi-label semi-supervised learning method, our method achieves better average precision and average recall when detecting complex events.
KeywordsHypergraph Trajectory Multi-label Pairwise fusion Spectral segmentation
This research has partially been supported by National Natural Science Foundation of China under Grant No. 41374129, 60673190 and 61203244, College Natural Science Research of Jiangsu Province under Grant No. 14KJB520008, Senior Technical Personnel of Scientific Research Fund of Jiangsu University under Grant No. 13JDG126.
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