The 7th AI City Challenge

• URL: http://arxiv.org/abs/2304.07500v1
• Date: Sat, 15 Apr 2023 08:02:16 GMT
• Title: The 7th AI City Challenge
• Authors: Milind Naphade, Shuo Wang, David C. Anastasiu, Zheng Tang, Ming-Ching Chang, Yue Yao, Liang Zheng, Mohammed Shaiqur Rahman, Meenakshi S. Arya, Anuj Sharma, Qi Feng, Vitaly Ablavsky, Stan Sclaroff, Pranamesh Chakraborty, Sanjita Prajapati, Alice Li, Shangru Li, Krishna Kunadharaju, Shenxin Jiang and Rama Chellappa
• Abstract summary: The AI City Challenge's seventh edition emphasizes two domains at the intersection of computer vision and artificial intelligence. The 2023 challenge had five tracks, which drew a record-breaking number of participation requests from 508 teams across 46 countries. The participating teams' top performances established strong baselines and even outperformed the state-of-the-art in the proposed challenge tracks.
• Score: 87.23137854688389
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The AI City Challenge's seventh edition emphasizes two domains at the intersection of computer vision and artificial intelligence - retail business and Intelligent Traffic Systems (ITS) - that have considerable untapped potential. The 2023 challenge had five tracks, which drew a record-breaking number of participation requests from 508 teams across 46 countries. Track 1 was a brand new track that focused on multi-target multi-camera (MTMC) people tracking, where teams trained and evaluated using both real and highly realistic synthetic data. Track 2 centered around natural-language-based vehicle track retrieval. Track 3 required teams to classify driver actions in naturalistic driving analysis. Track 4 aimed to develop an automated checkout system for retail stores using a single view camera. Track 5, another new addition, tasked teams with detecting violations of the helmet rule for motorcyclists. Two leader boards were released for submissions based on different methods: a public leader board for the contest where external private data wasn't allowed and a general leader board for all results submitted. The participating teams' top performances established strong baselines and even outperformed the state-of-the-art in the proposed challenge tracks.

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