Video Surveillance

Video surveillance research builds systems that detect people, vehicles, and events, track activity over time, and help operators search or review camera footage. Common approaches include object detectors and trackers, pose and motion analysis, weakly supervised anomaly detection, and newer vision-language models for describing scenes and answering questions. Methods are tested on settings such as traffic monitoring and anomaly benchmarks including UCF-Crime, XD-Violence, and ShanghaiTech, with growing attention to real-time edge deployment and privacy-preserving processing. A central challenge is making results reliable beyond familiar datasets: camera angle, image quality, location, and rare events can undermine performance, while privacy, useful detail, and human oversight must be balanced.