69 citations · 295 across the 55 of their papers we have counts for
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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…
Anatomy-Aware Unsupervised Detection and Localization of Retinal Abnormalities in Optical Coherence Tomography
Tania Haghighi, Sina Gholami, Hamed Tabkhi +1
Reliable automated analysis of Optical Coherence Tomography (OCT) imaging is crucial for diagnosing retinal disorders but faces a critical barrier: the need for expensive, labor-in…
EdgeVTP: Exploration of Latency-efficient Trajectory Prediction for Edge-based Embedded Vision Applications
Seungjin Kim, Reza Jafarpourmarzouni, Christopher Neff +2
Vehicle trajectory prediction is central to highway perception, but deployment on roadside edge devices necessitates bounded, deterministic end-to-end latency. We present EdgeVTP,…
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)…
Adversarially-Refined VQ-GAN with Dense Motion Tokenization for Spatio-Temporal Heatmaps
Gabriel Maldonado, Narges Rashvand, Armin Danesh Pazho +3
Continuous human motion understanding remains a core challenge in computer vision due to its high dimensionality and inherent redundancy. Efficient compression and representation a…
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…
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…
MoCLIP: Motion-Aware Fine-Tuning and Distillation of CLIP for Human Motion Generation
Gabriel Maldonado, Armin Danesh Pazho, Ghazal Alinezhad Noghre +2
Human motion generation is essential for fields such as animation, robotics, and virtual reality, requiring models that effectively capture motion dynamics from text descriptions.…
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…
MoFM: A Large-Scale Human Motion Foundation Model
Mohammadreza Baharani, Ghazal Alinezhad Noghre, Armin Danesh Pazho +2
Foundation Models (FM) have increasingly drawn the attention of researchers due to their scalability and generalization across diverse tasks. Inspired by the success of FMs and the…
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…
Multi-OCT-SelfNet: Integrating Self-Supervised Learning with Multi-Source Data Fusion for Enhanced Multi-Class Retinal Disease Classification
Fatema-E- Jannat, Sina Gholami, Jennifer I. Lim +3
In the medical domain, acquiring large datasets poses significant challenges due to privacy concerns. Nonetheless, the development of a robust deep-learning model for retinal disea…
Human-Centric Video Anomaly Detection Through Spatio-Temporal Pose Tokenization and Transformer
Ghazal Alinezhad Noghre, Armin Danesh Pazho, Hamed Tabkhi
Video Anomaly Detection (VAD) presents a significant challenge in computer vision, particularly due to the unpredictable and infrequent nature of anomalous events, coupled with the…
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…
OCT-SelfNet: A Self-Supervised Framework with Multi-Modal Datasets for Generalized and Robust Retinal Disease Detection
Fatema-E Jannat, Sina Gholami, Minhaj Nur Alam +1
Despite the revolutionary impact of AI and the development of locally trained algorithms, achieving widespread generalized learning from multi-modal data in medical AI remains a si…
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…
Real-Time Online Unsupervised Domain Adaptation for Real-World Person Re-identification
Christopher Neff, Armin Danesh Pazho, Hamed Tabkhi
Following the popularity of Unsupervised Domain Adaptation (UDA) in person re-identification, the recently proposed setting of Online Unsupervised Domain Adaptation (OUDA) attempts…
Real-World Community-in-the-Loop Smart Video Surveillance -- A Case Study at a Community College
Shanle Yao, Babak Rahimi Ardabili, Armin Danesh Pazho +3
Smart Video surveillance systems have become important recently for ensuring public safety and security, especially in smart cities. However, applying real-time artificial intellig…
A POV-based Highway Vehicle Trajectory Dataset and Prediction Architecture
Vinit Katariya, Ghazal Alinezhad Noghre, Armin Danesh Pazho +1
Vehicle Trajectory datasets that provide multiple point-of-views (POVs) can be valuable for various traffic safety and management applications. Despite the abundance of trajectory…