Frame Level Anomaly

Frame-level anomaly detection in videos aims to identify individual frames containing unusual events, a crucial task in applications like surveillance and industrial monitoring. Current research emphasizes weakly supervised or unsupervised learning approaches, often employing transformer-based architectures, autoencoders (including novel spatio-temporal variations), and techniques like pseudo-labeling to address the challenge of limited labeled data. These advancements are improving the accuracy and robustness of anomaly detection, particularly in handling diverse and complex anomalies beyond simple object detection, leading to more effective and efficient video analysis systems. The development of new, more representative datasets is also a key focus to better evaluate and improve model performance in real-world scenarios.