title: Laplacian eigenmap with temporal constraints for local abnormality detection in crowded scenes.
abstract: This paper addresses the problem of detecting and localizing abnormal activities in crowded scenes. A spatiotemporal Laplacian eigenmap method is proposed to extract different crowd activities from videos. This is achieved by learning the spatial and temporal variations of local motions in an embedded space. We employ representatives of different activities to construct the model which characterizes the regular behavior of a crowd. This model of regular crowd behavior allows the detection of abnormal crowd activities both in local and global contexts and the localization of regions which show abnormal behavior. Experiments on the recently published data sets show that the proposed method achieves comparable results with the state-of-the-art methods without sacrificing computational simplicity.
authors.forename: Myo
authors.initials: M
authors.lastname: Thida
authors.forename: How-Lung
authors.initials: HL
authors.lastname: Eng
authors.forename: Paolo
authors.initials: P
authors.lastname: Remagnino
mesh_terms: D056044:Actigraphy / Q000379:methods*; D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003441:Crowding; D003661:Decision Support Techniques; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*