5 citations · 9 across the 12 of their papers we have counts for
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YUTELA: A Scalable Edge Cloud AIoT System for Multi-Site Video Anomaly Detection with Scene Specific Adaptation
Shanle Yao, Babak Rahimi Ardabili, Amir Kimiyaie +1
Building upon our previous Ancilia framework, this work presents \emph{YUTELA}, an edge--server--cloud AIoT system for long-term, multi-site video anomaly detection. YUTELA replace…
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…
From Offline to Periodic Adaptation for Pose-Based Shoplifting Detection in Real-world Retail Security
Shanle Yao, Narges Rashvand, Armin Danesh Pazho +1
Shoplifting is a growing operational and economic challenge for retailers, with incidents rising and losses increasing despite extensive video surveillance. Continuous human monito…
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)…