Video anomaly detection identifies unusual behaviors or objects in video streams, crucial for public safety. Traditional methods often fail to capture subtle motion cues from raw frames, while recent deep learning models-though more effective-require heavy computational resources, limiting their real-time applicability. To address these challenges, we propose an unsupervised, lightweight, and efficient model: Flow-Enhanced Anomaly Detection (FEAD). Our key contribution is a novel optical flow-guided feature fusion mechanism that leverages pre-extracted optical flow information as a dynamic prior, effectively directing the raw image stream to focus on critical motion regions during feature extraction. By combining raw video frames and optical flow maps in a dual-stream architecture, FEAD predicts future frames and detects anomalies via reconstruction error. This approach enhances sensitivity to motion irregularities while maintaining low computational cost, making it suitable for real-time deployment in resource-constrained environments. We evaluated FEAD on three benchmark datasets-Ped2, Avenue, and ShanghaiTech-achieving AUC scores of 98.4%, 87.1%, and 75.6% respectively. In addition to strong accuracy, FEAD shows substantial advantages in inference speed and efficiency compared to existing methods, underscoring its practical value for real-world applications in surveillance and public safety.
Yin et al. (2025) studied this question.