Abnormal Activity Detection

Abnormal activity detection focuses on identifying deviations from expected patterns in various data streams, aiming to improve safety, efficiency, and healthcare. Current research emphasizes the use of deep learning models, including convolutional neural networks (for image and point cloud data), recurrent neural networks (for time series data), and neural radiance fields (for 3D model comparison), often enhanced by techniques like contrastive learning and adversarial training. These methods find application in diverse fields, ranging from industrial defect detection and urban safety monitoring to healthcare anomaly detection and network security, highlighting the broad impact of robust anomaly detection systems. The development of large, annotated datasets is also a key focus to improve model training and generalization.