Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Noghre, Ghazal Alinezhad"'
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 diverse and dynamic environments in which they occur. Human-centric V
Externí odkaz:
http://arxiv.org/abs/2408.15185
Autor:
Noghre, Ghazal Alinezhad, Yao, Shanle, Pazho, Armin Danesh, Ardabili, Babak Rahimi, Katariya, Vinit, Tabkhi, Hamed
PHEVA, a Privacy-preserving Human-centric Ethical Video Anomaly detection dataset. By removing pixel information and providing only de-identified human annotations, PHEVA safeguards personally identifiable information. The dataset includes seven indo
Externí odkaz:
http://arxiv.org/abs/2408.14329
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 considerable attention from the research community due to several inherent
Externí odkaz:
http://arxiv.org/abs/2406.15395
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-life settings poses significant challenges due to the dynamic nature
Externí odkaz:
http://arxiv.org/abs/2404.18747
Autor:
Ardabili, Babak Rahimi, Pazho, Armin Danesh, Noghre, Ghazal Alinezhad, Katariya, Vinit, Hull, Gordon, Reid, Shannon, Tabkhi, Hamed
Addressing public safety effectively requires incorporating diverse stakeholder perspectives, particularly those of the community, which are often underrepresented compared to other stakeholders. This study presents a comprehensive analysis of the co
Externí odkaz:
http://arxiv.org/abs/2312.06707
Autor:
Yao, Shanle, Ardabili, Babak Rahimi, Pazho, Armin Danesh, Noghre, Ghazal Alinezhad, Neff, Christopher, Bourque, Lauren, Tabkhi, Hamed
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 networks, leveraging recent advancements in AI for easy adoption. Prior
Externí odkaz:
http://arxiv.org/abs/2312.02078
Autor:
Katariya, Vinit, Jannat, Fatema-E, Pazho, Armin Danesh, Noghre, Ghazal Alinezhad, Tabkhi, Hamed
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 surge of the Internet of Things (IoT) in recent years, there has arisen
Externí odkaz:
http://arxiv.org/abs/2311.07880
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 to forecast a vehicle's future positions based on its past and curren
Externí odkaz:
http://arxiv.org/abs/2311.06623
Autor:
Yao, Shanle, Ardabili, Babak Rahimi, Pazho, Armin Danesh, Noghre, Ghazal Alinezhad, Neff, Christopher, Tabkhi, Hamed
Smart Video surveillance systems have become important recently for ensuring public safety and security, especially in smart cities. However, applying real-time artificial intelligence technologies combined with low-latency notification and alarming
Externí odkaz:
http://arxiv.org/abs/2303.12934
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 datasets, few offer a comprehensive and diverse range of driving scene
Externí odkaz:
http://arxiv.org/abs/2303.06202