Dual-Channel Autoencoder with Key Region Feature Enhancement for Video Anomalous Event Detection

Abstract

Video anomaly event detection is crucial for analyzing surveillance videos. Existing methods have limitations: frame-level detection fails to remove background interference, and object-level methods overlook object-environment interaction. To address these issues, this paper proposes a novel video anomaly event detection algorithm based on a dual-channel autoencoder with key region feature enhancement. The goal is to preserve valuable information in the global context while focusing on regions with a high anomaly occurrence. Firstly, a key region extraction network is proposed to perform foreground segmentation on video frames, eliminating background redundancy. Secondly, a dual-channel autoencoder is designed to enhance the features of key regions, enabling the model to extract more representative features. Finally, channel attention modules are inserted between each deconvolution layer of the decoder to enhance the model’s perception and discrimination of valuable information. Compared to existing methods, our approach accurately locates and focuses on regions with a high anomaly occurrence, improving the accuracy of anomaly event detection. Extensive experiments are conducted on the UCSD ped2, CUHK Avenue, and SHTech Campus datasets, and the results validate the effectiveness of the proposed method.