Graph formulation of video activities for abnormal activity recognition. (May 2017)

• Record Type: Journal Article
• Authors: Singh, Dinesh; Krishna Mohan, C.
• Is Part Of: Pattern recognition. Volume 65(2017:May)
• Page Start: 265
• Page End: 272
• Publication Date: 2017-05
• Subjects: Abnormal activity recognition -- Video activity classification -- Graph representation of video activity -- Graph kernel -- Bag-of-graphs (BoG); Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.01.001
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: 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. Abstract : Highlights: Abnormal activity recognition in surveillance videos. Graph representation of video activity to incorporate geometric structures along with motion and appearance information. Classification of activity graphs using graph kernel SVM in order to detect local abnormal activities. Global abnormal activity recognition using bag-of-graphs (BoG).