UCF-Crime

# Real-world Anomaly Detection in Surveillance Videos Surveillance videos are able to capture a variety of realistic anomalies. In this paper, we propose to learn anomalies by exploiting both normal and anomalous videos. To avoid annotating the anomalous segments or clips in training videos, which is very time consumin

Papers using UCF-Crime (6)

Evaluation Of Vision-llms In Surveillance Video2025QVAD: A Question-Centric Agentic Framework for Efficient and Training-Free Video Anomaly Detection2026Geometry-Aware Semantic Reasoning for Training Free Video Anomaly Detection2026Enhancing Weakly Supervised Multimodal Video Anomaly Detection through Text Guidance2026Federated Learning For Video Violence Detection: Complementary Roles Of Lightweight Cnns And Vision-language Models For Energy-efficient Use2025Unlocking Vision-language Models For Video Anomaly Detection Via Fine-grained Prompting2025