Fast video anomaly detection via context-aware shortcut exploration and abnormal feature distance learning

Surveillance systems are essential in computer vision, with video anomaly detection (VAD) critical for real-world security. Conventional approaches anticipate abnormal frames using normal patterns learned with normal training data and determine anomalies by comparing the output and input. However, they often incur high computational costs due to pre-trained networks and complex modules. Also, they suffer from the tendency to overlook anomalies due to strong generalizing ability. To address these, we propose two innovations: Anomaly Distance Learning (ADL) and Context-Aware Skip Connection (CONASkip), maximizing the distinction between normal and abnormal samples. ADL increases the feature gap by learning to separate normal and abnormal features, while CONASkip selectively connects layers to preserve normal information and differentiate output quality. These methods operate on self-supervised signals, replacing complex and costly modules. Additionally, ADL is excluded during testing, enabling fast inference. Our model achieves 130 frames per second and superior performance on three benchmarks, addressing real-world efficiency challenges.