Meng Yang, Sutharshan Rajasegarar, Aravinda S. Rao, Christopher Leckie, Marimuthu Palaniswami
Intelligent Information Processing VIII: IFIP Advances in Information and Communication Technology, vol 486, pp. 132-141
Publication year: 2016

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.