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Learning Video Manifolds for Content Analysis of Crowded Scenes

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Thida, Myo, How-Lung , Eng, Monekosso, Dorothy and Remagnino, Paolo (2012) Learning Video Manifolds for Content Analysis of Crowded Scenes. IPSJ Transactions on Computer Vision and Applications, 4 . pp. 71-77. [Journal article]

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URL: https://www.jstage.jst.go.jp/article/ipsjtcva/4/0/4_71/_article

DOI: 10.2197/ipsjtcva.4.71

Abstract

In this paper, we propose a new approach for recognizing group events and abnormality detection in a crowded scene. A manifold learning algorithm with temporal-constraints is proposed to embed a video of a crowded scene in a low-dimensional space. Our low dimensional representation of a video preserves the spatial temporal property of a video as well as the characteristic of the video. Recognizing video events and abnormality detection in a crowded scene is achieved by studying the video trajectory in the manifold space. We evaluate our proposed method on the state-of-the-art public data-sets containing different crowd events. Qualitative and quantitative results show the promising performance of the proposed method.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Smart Environments
ID Code:22325
Deposited By:Dr Dorothy Monekosso
Deposited On:10 Jul 2012 13:09
Last Modified:10 Jul 2012 13:09

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