Enhancing video anomaly detection through dual-path reconstruction and diverse pseudo-anomaly generation

Anomaly Detection in Surveillance Video: An Unsupervised Unity Encoder Approach

A unique encoder-decoder based anomaly detection framework that incorporates skip connections to preserve spatial features during reconstruction and Tanh activation in the bottleneck for a balanced latent space to improve sensitivity to fine anomalies is proposed.

YOLO world guides open world video anomaly detection

Experimental results show that the evaluation quantifies both benchmark anomaly detection and direct known-versus-unknown separation under the declared protocols, and that object prompts dominate the fused decision, while the auxiliary prompt groups do not yet contribute positively under the held-out-category protocol.

Action-aware anomaly detection in surveillance videos using normal behavior modeling

Qualitative and quantitative results confirm that modeling normal action semantics provides a robust and scalable solution for real-world surveillance anomaly detection.

Spatio-temporal video anomaly detection via CNN-ViT autoencoder and farneback optical flow

This research introduces a novel memory-augmented CNN-ConvViT autoencoder framework for unsupervised video anomaly detection and introduces a Temporal-Aware Prototype Memory Module (TAPMM) that explicitly learns normal spatio-temporal behavior patterns.

TASG-VAD: Weakly Supervised Video Anomaly Detection via Temporal Variation Attention and Adaptive Saliency Guidance

To alleviate the model’s reliance on dominant anomalous segments, TASG-VAD introduces an Adaptive Saliency Guidance (ASG) strategy, which performs intra-video saliency ranking and dynamic masking to guide the model toward overlooked subtle anomalies.

Detection and Identification of Anomalous Events in Videos Using Deep Learning-Based Anomaly Detection