Real-time anomaly detection for ‘Remote’ bus stop surveillance using unsupervised conditional generative adversarial networks

In response to the imbalance between normal and abnormal samples in existing anomaly detection datasets, as well as the complexity in defining anomalies, we introduce a new dataset named Remote Stop to provide data support for existing algorithms. Concurrently, we propose an unsupervised video anomaly detection method based on conditional generative adversarial networks. Our approach trains the model to learn the distribution of normal video data, enabling it to identify anomalous events. The incorporation of a spatial attention mechanism enhances the model’s performance in detecting abnormal behaviors in video frames while maintaining high processing efficiency. Moreover, unlike other methods that assess the entire image, our approach uses overlapping image blocks to determine anomalies, enhancing the accuracy and robustness of the model in image segmentation. These innovations not only address the issues of scarce samples and high-cost labeling but also provide new perspectives and tools for video anomaly detection in the field of public safety. The effectiveness of the model was validated on the Avenue and Ped2 datasets and applied to our newly created dataset (Remote Stop), achieving an AUC of 84.3% and processing 61 video frames per second. This enables efficient sequential processing of large-scale video data, offering positive contributions to enhancing public road safety by providing early warnings and enabling timely preventive measures.