Trajectory based abnormal event detection in video traffic surveillance using general potential data field with spectral clustering

Detection of abnormal trajectories in a traffic scene is an important problem in Video Traffic Surveillance (VTS). Recently, General Potential Data Field (GPDf)-based trajectory clustering scheme has been adopted for detecting abnormal events such as illegal U-turn, wrong side and unusual driving behaviors and it uses spatial and temporal attributes explicitly. The concept of data field is used to discover the relation between the spatial points in data-space and grouping them into clusters based on their mutual interaction. Existing methodologies related to potential data field-based clustering have certain limitations such as pre-defined cluster size, non-effective cluster center identification, and limitation in range estimation using isotropic impact factor (h) which leads to inaccurate results. In order to address the above-mentioned issues, this paper proposes an efficient anomaly detection scheme based on General Potential Data field with Spectral Clustering (GPDfSC). The proposed GPDfSC scheme utilizes potential data field technique along with spectral clustering for effective identification of abnormalities. The Limitation in impact factor(h) is overcome by using anisotropic impact parameter Bmat. Further, Bayesian Decision theory is used to classify the events as normal or abnormal. The proposed scheme is implemented in real time using GPU and from the results it is found that it gives 12% better accuracy in detecting abnormalities than the state of art technique.