Graph formulation of video activities for abnormal activity recognition

Singh, D and C, Krishna Mohan (2017) Graph formulation of video activities for abnormal activity recognition. Pattern Recognition, 65. pp. 265-272. ISSN 0031-3203

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Abnormal activity recognition is a challenging task in surveillance videos. In this paper, we propose an approach for abnormal activity recognition based on graph formulation of video activities and graph kernel support vector machine. The interaction of the entities in a video is formulated as a graph of geometric relations among space–time interest points. The vertices of the graph are spatio-temporal interest points and an edge represents the relation between appearance and dynamics around the interest points. Once the activity is represented using a graph, then for classification of the activities into normal or abnormal classes, we use binary support vector machine with graph kernel. These graph kernels provide robustness to slight topological deformations in comparing two graphs, which may occur due to the presence of noise in data. We demonstrate the efficacy of the proposed method on the publicly available standard datasets viz. UCSDped1, UCSDped2 and UMN. Our experiments demonstrate high rate of recognition and outperform the state-of-the-art algorithms.

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Item Type: Article
Uncontrolled Keywords: Abnormal activity recognition; Video activity classification; Graph representation of video activity; Graph kernel; Bag-of-graphs (BoG)
Subjects: Computer science > Big Data Analytics
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 10 Jan 2017 04:22
Last Modified: 01 Sep 2017 09:01
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