Video Surveillance

Video surveillance research focuses on developing automated systems for analyzing video data to enhance security and safety. Current efforts concentrate on improving the accuracy and efficiency of tasks like person re-identification, anomaly detection, and action recognition, employing deep learning models such as transformers, graph convolutional networks, and various convolutional neural networks tailored to specific applications (e.g., violence detection, gait recognition). These advancements are significant for improving public safety, optimizing resource allocation in security systems, and enabling more effective analysis of large-scale video datasets. Furthermore, research addresses ethical considerations, such as privacy preservation and bias mitigation, within these systems.