Video Anomaly Detection

Video anomaly detection (VAD) aims to automatically identify unusual events in video footage, a crucial task for security, surveillance, and autonomous driving. Current research emphasizes developing robust methods that generalize well across different datasets and scenarios, focusing on techniques like autoencoders, transformers, and graph neural networks, often incorporating multimodal data (RGB, optical flow, audio) and leveraging pre-trained large language and vision models for improved accuracy and explainability. The field's impact stems from its potential to enhance safety and security in various applications by automating the detection of anomalous activities that might otherwise go unnoticed.