Enhancing video anomaly detection with improved UNET and cascade sliding window technique

Computer vision video anomaly detection still needs to be improved, especially when identifying images with unusual motions or objects. Current approaches mainly concentrate on reconstruction and prediction methods, and unsupervised video anomaly detection faces difficulties because there are not enough tagged abnormalities, which reduces accuracy. This paper presents a novel framework called the Improved UNET (I-UNET), designed to counteract overfitting by addressing the need for complex models that can extract subtle information from video anomalies. Video frame noise can be eliminated by preprocessing the frames with a Weiner filter. Moreover, the system uses Convolution Long Short-Term Memory (ConvLSTM) layers to smoothly integrate temporal and spatial data into its encoder and decoder portions, improving the accuracy of anomaly identification. The Cascade Sliding Window Technique (CSWT) is used post-processing to identify anomalous frames and generate anomaly scores. Compared to baseline approaches, experimental results on the UCF, UCSDped1, and UCSDped2 datasets demonstrate notable performance gains, with 99% accuracy, 90.8% Area Under Curve (AUC), and 10.9% Equal Error Rate (EER). This study provides a robust and accurate framework for video anomaly detection with the highest accuracy rate.