Video Anomaly

Video anomaly detection (VAD) focuses on automatically identifying unusual events in video sequences, aiming to improve security, surveillance, and industrial monitoring. Current research emphasizes developing robust models that handle diverse anomaly types, limited labeled data (through techniques like weakly supervised learning and pseudo-labeling), and the need for explainable results, often leveraging deep learning architectures such as autoencoders, transformers, and large language models (LLMs) for feature extraction, anomaly scoring, and even generating textual explanations. The field's significance lies in its potential to automate anomaly detection in various applications, reducing human workload and improving the efficiency and accuracy of monitoring systems.