READ3D-Net: Residual Autoencoder and GAN-Based 3-D Convolutional Network for Anomaly Detection

Video anomaly detection (VAD) is of great importance for a variety of real-time applications in video surveillance. Most deep learning-based anomaly detection algorithms adopt a one-class learning scheme to train a classifier using only normal data to distinguish between normal and abnormal events during the test phase. However, these methods, whether they are reconstruction or prediction models, commonly face the challenge of the model's overly strong representation capability, which leads to excessive fitting of abnormal events and thus limits the performance of the model in diverse scenarios. To address these challenges, this work develops a novel residual autoencoder and generative adversarial network-based 3-D convolutional network, called READ3D-net, for anomaly detection. An adaptive multimodal pseudoanomaly generator is developed to simulate and generate diverse pseudoanomalies, aiming to enhance the model's ability to extract the features with regard to 'abnormal' behaviors while reducing the interference of background on the detection performance. In addition, residual structures are incorporated into the design of a reconstruction-based autoencoder model to enhance its feature extraction and discriminative capabilities. To further improve the model's reconstruction ability on normal data, a dynamic generative adversarial strategy is proposed for effective feature learning. Extensive experiments conducted on public benchmark datasets demonstrate that the proposed model is more competitive than the state-of-the-art methods in video anomaly detection tasks, fully validating the effectiveness and practicality of the proposed approach.