Efficient and Supervised Anomalous Event Detection in Videos for Surveillance Purposes

In this paper, we consider the challenging problem of anomalous behavior detection in videos. Considering the pixel based anomaly detection, we have used k-mean clustering, a posteriori probability based probabilistic model, and region intersection to detect the anomalies in the video. The proposed technique considers the normal events as the events of higher probabilities. Densely sampled points are passed to a probabilistic model through k-mean clustering to obtain the probability of events. A threshold on the probability values is applied to distinguish anomaly from normal events. The final results of anomalous event detection obtained from different spatial scales are combined through region intersection. The integration of results of multi-scale anomaly detection using region intersection reduces false positives robustly. We have tested our technique over publically available standard video anomaly dataset.