TL;DR
This paper introduces a VAD method leveraging a multi-source pseudo-anomaly generator and a dual-decoder framework that integrates spatiotemporal perturbations and object-level local appearance perturbations to enhance anomaly coverage.
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.
Qualitative and quantitative results confirm that modeling normal action semantics provides a robust and scalable solution for real-world surveillance anomaly detection.
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.
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.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
