Effective Abnormal Behavior Identification In A Crowd

Effective analysis of crowd behavior is very challenging issue in video surveillance system. Most of the conventional approach is not up to the mark for real time behavior analysis. In this work, we have proposed an optical flow method along with KNCN classification approach to recognize abnormality in a video scene. Optical flow is a global motion information method which works efficiently. Optical flow vector provides direction and displacement of a point between two consecutive frames. This feature vector acts as input for KNN classifier. A surrounding neighbor (SN) concept is used for finding k-Nearest Centroid Neighbors(k-NCN) for each frame. We assign the class level based on the votes of k-NCN. The obtained result is promising and seems very efficient in terms of accuracy. We analyzed result and shown frame level comparison with ground truth. ROC analysis provides area under curve (AUC) for different data sequences and is compared with other state of the art techniques. Our proposed KNCN-OF method outperforms all other state of the art techniques with an accuracy of 99%.