Ulster University Logo

Learning Video Manifolds for Content Analysis of Crowded Scenes

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]

Full text not available from this repository.

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

DOI: 10.2197/ipsjtcva.4.71


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 > Smart Environments
Computer Science Research Institute
ID Code:22325
Deposited By: Dr Dorothy Monekosso
Deposited On:10 Jul 2012 12:09
Last Modified:10 Jul 2012 12:09

Repository Staff Only: item control page