Authors: Victor Adewopo, Nelly Elsayed, Zag Elsayed, Murat Ozer, Constantinos Zekios, Ahmed Abdelgawad, Magdy Bayoumi
Published on: January 07, 2024
Impact Score: 8.12
Arxiv code: Arxiv:2401.03587
Summary
- What is new: A novel comprehensive dataset for traffic accident detection integrating data from various global sources to improve action recognition systems.
- Why this is important: The difficulty of interpreting rapid and variable actions in diverse conditions for traffic accident detection in smart cities.
- What the research proposes: Creating a benchmark dataset for computer vision and action recognition systems that can predict and detect road traffic accidents more accurately.
- Results: The dataset is expected to enhance both academic research and real-time accident detection applications, contributing to smarter, safer cities.
Technical Details
Technological frameworks used:
Models used: Action recognition models, machine learning algorithms
Data used: Datasets from various road networks, weather conditions, and regions across the globe
Potential Impact
Smart city technology providers, traffic management systems, public safety and surveillance companies, automotive industry, AI and machine learning platforms
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