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.
|Journal||IPSJ Transactions on Computer Vision and Applications|
|Publication status||Published - 30 May 2012|
Thida, M., How-Lung, E., Monekosso, D., & Remagnino, P. (2012). Learning Video Manifolds for Content Analysis of Crowded Scenes. IPSJ Transactions on Computer Vision and Applications, 4, 71-77. https://doi.org/10.2197/ipsjtcva.4.71