Time-Efficient Video Anomaly Detection With Parallel Computing and Twice-Reconstruction

To tackle two prevalent challenges in video anomaly detection, misclassification of abnormal frames as normal and the prolonged runtime of existing methods, this article proposes a time-efficient anomaly detection method utilizing twice-reconstruction and parallel computing. The proposed method integrates three key innovations: a twice-reconstruction model to amplify the reconstruction error for abnormal frames, a memory module to better capture and utilize normal distribution patterns, and a parallel computing strategy to significantly accelerate anomaly detection. By reconstructing frames twice, our method enhances the model’s anomaly detection capabilities. Meanwhile, a memory module is incorporated to retain normal distribution patterns more effectively, reducing the likelihood of misclassifying abnormal frames. Furthermore, parallel computing is employed to minimize runtime and boost detection efficiency. Unlike existing methods, TPR-VAD achieves both high detection accuracy and time efficiency, making it well-suited for real-world applications such as intelligent surveillance systems. Experimental results on the UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets show that our method achieves superior detection accuracy, reaching 99.01%, 91.21%, and 82.77%, respectively, while significantly reducing runtime.