2 citations · 7 across the 11 of their papers we have counts for
11 papers
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…
A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environment
Ghazal Alinezhad Noghre, Armin Danesh Pazho, Hamed Tabkhi
Video Anomaly Detection (VAD) has emerged as a pivotal task in computer vision, with broad relevance across multiple fields. Recent advances in deep learning have driven significan…
Shopformer: Transformer-Based Framework for Detecting Shoplifting via Human Pose
Narges Rashvand, Ghazal Alinezhad Noghre, Armin Danesh Pazho +2
Shoplifting remains a costly issue for the retail sector, but traditional surveillance systems, which are mostly based on human monitoring, are still largely ineffective, with only…
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…
An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Prediction
Ghazal Alinezhad Noghre, Armin Danesh Pazho, Hamed Tabkhi
Video Anomaly Detection (VAD) represents a challenging and prominent research task within computer vision. In recent years, Pose-based Video Anomaly Detection (PAD) has drawn consi…
Evaluating the Effectiveness of Video Anomaly Detection in the Wild: Online Learning and Inference for Real-world Deployment
Shanle Yao, Ghazal Alinezhad Noghre, Armin Danesh Pazho +1
Video Anomaly Detection (VAD) identifies unusual activities in video streams, a key technology with broad applications ranging from surveillance to healthcare. Tackling VAD in real…
Exploring Public's Perception of Safety and Video Surveillance Technology: A Survey Approach
Babak Rahimi Ardabili, Armin Danesh Pazho, Ghazal Alinezhad Noghre +4
Addressing public safety effectively requires incorporating diverse stakeholder perspectives, particularly those of the community, which are often underrepresented compared to othe…
From Lab to Field: Real-World Evaluation of an AI-Driven Smart Video Solution to Enhance Community Safety
Shanle Yao, Babak Rahimi Ardabili, Armin Danesh Pazho +4
This article adopts and evaluates an AI-enabled Smart Video Solution (SVS) designed to enhance safety in the real world. The system integrates with existing infrastructure camera n…
VegaEdge: Edge AI Confluence Anomaly Detection for Real-Time Highway IoT-Applications
Vinit Katariya, Fatema-E- Jannat, Armin Danesh Pazho +2
Vehicle anomaly detection plays a vital role in highway safety applications such as accident prevention, rapid response, traffic flow optimization, and work zone safety. With the s…
VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformer
Armin Danesh Pazho, Ghazal Alinezhad Noghre, Vinit Katariya +1
Enhancing roadway safety has become an essential computer vision focus area for Intelligent Transportation Systems (ITS). As a part of ITS, Vehicle Trajectory Prediction (VTP) aims…