Enhancement of PSNR based Anomaly Detection in Surveillance Videos using Penalty Modules

One of the desirable features of a surveillance system is the automatic identification of anomalous events in surveillance videos. The recent approaches for anomalous events identification utilize the difference between the predicted future frame and the current frame to detect the frames with an anomalous event. However, these approaches fare poorly if there is an overlap between multiple objects present in the scene. This work proposes to incorporate two modules to the future frame prediction-based anomalous activity detection approach. The first module penalizes the frame-wise PSNR value if there is an overlap between a normal and an anomalous object. In contrast, the second module penalizes the PSNR value if there is a sudden deviation of the vehicles from its trajectory. This object-centric approach ensures that the anomalous events are correctly identified even in the presence of occlusion. The proposed method is evaluated on two standard datasets Ped 2 and CUHK Avenue. The proposed method outperforms the existing approaches, and an AUC of 96.2% and 85.22% is obtained on Ped2 and CUHK, respectively.