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 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.
Intelligent Information Processing