Advances in Deep Learning for Video Anomaly Detection: A Comprehensive Review
Rajat Gupta, Nidhi Tyagi · 2025
Video-based anomaly detection plays a crucial role in applications like surveillance, healthcare, and autonomous systems. Deep learning techniques, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, have significantly improved the ability to detect unusual human activities by modeling complex spatial-temporal patterns. However, challenges remain, including the need for large labeled datasets, computational demands for real-time performance, and limited model interpretability. This review highlights advancements in deep learning for anomaly detection, examines datasets and evaluation metrics, and identifies opportunities to address existing challenges for real-world deployment.