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Anomalous Behavior Detection in Crowded Scenes Using Clustering and Spatio-Temporal Features

Abstract : Anomalous behavior detection in crowded and unanticipated scenarios is an important problem in real-life applications. Detection of anomalous behaviors such as people standing statically and loitering around a place are the focus of this paper. In order to detect anomalous events and objects, ViBe was used for background modeling and object detection at first. Then, a Kalman filter and Hungarian cost algorithm were implemented for tracking and generating trajectories of people. Next, spatio-temporal features were extracted and represented. Finally, hyperspherical clustering was used for anomaly detection in an unsupervised manner. We investigate three different approaches to extracting and representing spatio-temporal features, and we demonstrate the effectiveness of our proposed feature representation on a standard benchmark dataset and a real-life video surveillance environment.
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Submitted on : Wednesday, October 11, 2017 - 4:58:13 PM
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Meng Yang, Sutharshan Rajasegarar, Aravinda Rao, Christopher Leckie, Marimuthu Palaniswami. Anomalous Behavior Detection in Crowded Scenes Using Clustering and Spatio-Temporal Features. 9th International Conference on Intelligent Information Processing (IIP), Nov 2016, Melbourne, VIC, Australia. pp.132-141, ⟨10.1007/978-3-319-48390-0_14⟩. ⟨hal-01614999⟩



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