Electrical and Electronic Engineering - Research Publications

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

Yang, M ; Rajasegarar, S ; Rao, AS ; Leckie, C ; Palaniswami, M ; Shi, Z ; Vadera, S ; Li, G (Springer, 2016)

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.
A visual-numeric approach to clustering and anomaly detection for trajectory data

Kumar, D ; Bezdek, JC ; Rajasegarar, S ; Leckie, C ; Palaniswami, M (SPRINGER, 2017-03)