Anomalous Behavior Detection in Crowded Scenes Using Clustering and Spatio-Temporal Features
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 t
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Department of Computing and Information Systems, The University of Melbourne, Melbourne, VIC 3010, Australia [email protected], [email protected] 2 National ICT Australia (NICTA), Melbourne, VIC 3053, Australia 3 School of Information Technology, Deakin University, Melbourne, VIC 3125, Australia [email protected] 4 Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia {aravinda.rao,palani}@unimelb.edu.au
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. Keywords: Anomaly detection clustering
Spatio-temporal features
Hyperspherical
1 Introduction Analysis of human behaviour in crowded environment is an important and challenging task for video surveillance. Significant efforts have been made to solve this task, such as using large numbers of surveillance cameras to monitor human behaviour. However, the ubiquity of cameras still causes issues, such as system overload, manual monitoring and low accuracy. Therefore, an automated system for behaviour detection is required to help improve efficiency and reduce detection errors. We aim to detect anomalous events in a target area monitored by cameras over a period of time. Anomalous events © IFIP International Federation for Information Processing 2016 Published by Springer International Publishing AG 2016. All Rights Reserved Z. Shi et al. (Eds.): IIP 2016, IFIP AICT 486, pp. 132–141, 2016. DOI: 10.1007/978-3-319-48390-0_14
Anomalous Behavior Detection in Crowded Scenes
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include standing statically, loitering around a place, running among a crowd of walking people, and the number of people increasing dramatically at the entrance or exit in some stadium, cinema or other venue. These abnormal events can occur suddenly, hence, an automated and online analysis system is needed for detecting anomalous behaviours. In this paper, we construct a framework for anomalous behaviour detection, such as remaining static or loitering in the flow of a crowd. This method is almost real-time. In particular, we use a hyperspherical clustering method on the encoded trajectories of pedestrians using novel spatio-temporal feature representations. In
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