Physical Surveillance

Physical surveillance research focuses on developing and improving automated systems for monitoring and analyzing visual data, primarily aiming to enhance security and safety. Current research emphasizes the use of computer vision and machine learning, employing techniques like deep learning, perceptual hashing, and spatio-temporal graph convolutional networks to achieve tasks such as anomaly detection, pedestrian tracking, and action recognition. These advancements have significant implications for various applications, including autonomous driving, crime prevention, and public health, while also raising ethical concerns regarding privacy and potential misuse.