9 papers · 1 filter
VQ-VAD: Vector-quantized Motion Representation Learning for Human-centric Video Anomaly Detection
Narges Rashvand, Ghazal Alinezhad Noghre, Shanle Yao +2
Video Anomaly Detection (VAD) is inherently challenging due to the scarcity of anomalies and the large visual variability in surveillance footage, including changes in lighting, vi…
From Frames to Events: Rethinking Evaluation in Human-Centric Video Anomaly Detection
Narges Rashvand, Shanle Yao, Armin Danesh Pazho +2
Pose-based Video Anomaly Detection (VAD) has gained significant attention for its privacy-preserving nature and robustness to environmental variations. However, traditional frame-l…
Are Multimodal LLMs Ready for Surveillance? A Reality Check on Zero-Shot Anomaly Detection in the Wild
Shanle Yao, Armin Danesh Pazho, Narges Rashvand +1
Multimodal large language models (MLLMs) have demonstrated impressive general competence in video understanding, yet their reliability for real-world Video Anomaly Detection (VAD)…
ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholds
Shanle Yao, Ghazal Alinezhad Noghre, Armin Danesh Pazho +1
Video Anomaly Detection (VAD) can play a key role in spotting unusual activities in video footage. VAD is difficult to use in real-world settings due to the dynamic nature of human…
Exploring Pose-Based Anomaly Detection for Retail Security: A Real-World Shoplifting Dataset and Benchmark
Narges Rashvand, Ghazal Alinezhad Noghre, Armin Danesh Pazho +2
Shoplifting poses a significant challenge for retailers, resulting in billions of dollars in annual losses. Traditional security measures often fall short, highlighting the need fo…
Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmark
Armin Danesh Pazho, Shanle Yao, Ghazal Alinezhad Noghre +3
Human-centric Video Anomaly Detection (VAD) aims to identify human behaviors that deviate from normal. At its core, human-centric VAD faces substantial challenges, such as the comp…