Video Frame Anomaly Behavior Detection Based on Foreground Area Generative Adversarial Networks

In order to improve the accuracy of video anomaly behavior detection, a new detection method based on generative adversarial networks for video foreground areas is proposed. First, the foreground and background masks of the ground truth video frame are extracted, to determine the foreground areas of the output video frames from generative adversarial networks. For the foreground areas under consideration, the foreground area peak signal-to-noise ratio (F-PSNR) is used to calculate the detection score of anomaly behaviors. The experimental results show that the proposed method can effectively improve the detection accuracy of video anomaly behaviors with a reduced detection time for the Avenue dataset, UCSD-Ped1 dataset and UCSD-Ped2 dataset.