Abstract
Video anomaly detection is an essential task because of its numerous applications in various areas. Because of the rarity of abnormal events and the complicated characteristic of videos, video anomaly detection is challenging and has been studied for a long time. In this paper, we propose a semi-supervised approach with a dual discriminator-based generative adversarial network structure. Our method considers more motion information in video clips compared with previous approaches. Specifically, in the training phase, we predict future frames for normal events via a generator and attempt to force the predicted frames to be similar to their ground truths. In addition, we utilize both a frame discriminator and motion discriminator to adverse the generator to generate more realistic and consecutive frames. The frame discriminator attempts to determine whether the input frames are generated or original frames sampled from the normal video. The motion discriminator attempts to determine whether the given optical flows are real or fake. Fake optical flows are estimated from generated frames and adjacent frames, and real optical flows are estimated from the real frames sampled from original videos. Then, in the testing phase, we evaluate the quality of predicted frames to obtain the regular score, and we consider those frames with lower prediction qualities as abnormal frames. The results of experiments on three publicly available datasets demonstrate the effectiveness of our proposed method.
Highlights
Video anomaly detection has long been studied because of its essential applications in various areas, such as traffic monitors, violence alerts, and smoke alarms
We apply the idea of the generative adversarial network (GAN) for video anomaly detection, and our framework is composed of a generator and two discriminators, which discriminate the true and false from the appearance and motion, respectively
In this paper, we propose a dual discriminator-based GAN structure for video anomaly detection and perform experiments on datasets with different scales, which proves the effectiveness of our approach
Summary
Video anomaly detection has long been studied because of its essential applications in various areas, such as traffic monitors, violence alerts, and smoke alarms. With the emergence of the generative adversarial network (GAN) [10], the performance of future frame prediction has been greatly improved, and the frame prediction-based method has achieved the stateof-the-art performance for semi-supervised video anomaly detection. According to the learned optical flow distribution of the normal behavior, a Markov random field (MRF) based on time and space information can be further constructed They mapped the nodes in the MRF graph one-to-one with the spatial-temporal grid in the frame to detect if there is any abnormal behavior in the video. The generated frame at time t is symbolized as It , and its ground truth is denoted It. The training objective (J ) of our model f is to learn the regularity of normal events, and those events deviating from it are considered to be anomalies. The appearance loss includes intensity loss, gradient loss, and adversarial training loss, and the motion loss includes optical flow loss and adversarial training loss with respect to motion information
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