Abnormal Event Detection

Abnormal event detection aims to automatically identify unusual occurrences within video or other sequential data, a task complicated by the inherent variability of "abnormality." Current research heavily utilizes deep learning approaches, including autoencoders, generative adversarial networks (GANs), and masked autoencoders, often incorporating techniques like contrastive learning and transfer learning to improve efficiency and generalization across diverse datasets. This field is crucial for enhancing public safety through improved surveillance systems, optimizing resource allocation in various domains, and providing insights into complex systems like software development processes.