Authors: Shuo Wang, David C. Anastasiu, Zheng Tang, Ming-Ching Chang, Yue Yao, Liang Zheng, Mohammed Shaiqur Rahman, Meenakshi S. Arya, Anuj Sharma, Pranamesh Chakraborty, Sanjita Prajapati, Quan Kong, Norimasa Kobori, Munkhjargal Gochoo, Munkh-Erdene Otgonbold, Fady Alnajjar, Ganzorig Batnasan, Ping-Yang Chen, Jun-Wei Hsieh, Xunlei Wu, Sameer Satish Pusegaonkar, Yizhou Wang, Sujit Biswas, Rama Chellappa

Published on: April 15, 2024

Impact Score: 7.6

Arxiv code: Arxiv:2404.09432

Summary

• What is new: Introduction of new tracks focusing on dense video captioning for traffic safety, naturalistic driving analysis, fish-eye camera analytics, and motorcycle helmet rule violation detection.
• Why this is important: The need for advancements in computer vision and AI for applications in retail, warehouses, and Intelligent Traffic Systems.
• What the research proposes: Implementing a multi-faceted AI City Challenge that encompasses various aspects of real-world scenarios such as people tracking, traffic safety, driver behavior analysis, and rule violation detection.
• Results: Participants achieved unprecedented interest and set new benchmarks, some surpassing state-of-the-art achievements in the field.

Technical Details

Technological frameworks used: Multi-target multi-camera tracking, dense video captioning, naturalistic driving analysis, fish-eye camera analytics.

Models used: Models for 3D annotation, camera matrices, online tracking algorithms, and specific sets for each track challenge.

Data used: FishEye8K dataset, multi-camera feeds for traffic analysis, and dataset for motorcycle helmet rule violation.

Potential Impact

Retail, warehouse management systems, insurance companies focused on traffic accidents, companies specializing in driver safety, and ITS solution providers.

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