Surveillance Video

Surveillance video analysis focuses on automatically detecting and classifying events within video streams, primarily for security and safety applications. Current research emphasizes developing robust and generalizable deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs like ConvLSTM), transformer networks, and generative adversarial networks (GANs), often incorporating techniques like transfer learning and multiple instance learning to address challenges such as limited data and class imbalance. These advancements improve the accuracy and efficiency of anomaly detection, activity recognition, and object tracking, with significant implications for public safety, crime prevention, and traffic management.