The 8th AI City Challenge

• URL: http://arxiv.org/abs/2404.09432v1
• Date: Mon, 15 Apr 2024 03:12:17 GMT
• Title: The 8th AI City Challenge
• 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,
• Abstract summary: The 2024 edition featured five tracks, attracting unprecedented interest from 726 teams in 47 countries and regions. The challenge utilized two leaderboards to showcase methods, with participants setting new benchmarks.
• Score: 57.25825945041515
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (ITS), presenting significant research opportunities. The 2024 edition featured five tracks, attracting unprecedented interest from 726 teams in 47 countries and regions. Track 1 dealt with multi-target multi-camera (MTMC) people tracking, highlighting significant enhancements in camera count, character number, 3D annotation, and camera matrices, alongside new rules for 3D tracking and online tracking algorithm encouragement. Track 2 introduced dense video captioning for traffic safety, focusing on pedestrian accidents using multi-camera feeds to improve insights for insurance and prevention. Track 3 required teams to classify driver actions in a naturalistic driving analysis. Track 4 explored fish-eye camera analytics using the FishEye8K dataset. Track 5 focused on motorcycle helmet rule violation detection. The challenge utilized two leaderboards to showcase methods, with participants setting new benchmarks, some surpassing existing state-of-the-art achievements.

Related papers

• The 7th AI City Challenge [87.23137854688389]
  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.
  arXiv  Detail & Related papers  (2023-04-15T08:02:16Z)
• The 6th AI City Challenge [91.65782140270152]
  The 4 challenge tracks of the 2022 AI City Challenge received participation requests from 254 teams across 27 countries. The top performance of participating teams established strong baselines and even outperformed the state-of-the-art in the proposed challenge tracks.
  arXiv  Detail & Related papers  (2022-04-21T19:24:17Z)
• Scalable and Real-time Multi-Camera Vehicle Detection, Re-Identification, and Tracking [58.95210121654722]
  We propose a real-time city-scale multi-camera vehicle tracking system that handles real-world, low-resolution CCTV instead of idealized and curated video streams. Our method is ranked among the top five performers on the public leaderboard.
  arXiv  Detail & Related papers  (2022-04-15T12:47:01Z)
• City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones [28.922703073971466]
  This paper describes our solution to the Track 3 multi-camera vehicle tracking task in 2021 AI City Challenge (AICITY21) The framework includes:. Use mature detection and vehicle re-identification models to extract targets and appearance features. According to the characteristics of the crossroad, the Tracklet Filter Strategy and the Direction Based Temporal Mask are proposed.
  arXiv  Detail & Related papers  (2021-05-14T03:01:17Z)
• Video Surveillance for Road Traffic Monitoring [0.0]
  This paper presents the learned techniques during the Video Analysis Module of the Master in Computer Vision from the Universitat Autonoma de Barcelona. The challenge aims to track vehicles across multiple cameras placed in multiple intersections spread out over a city.
  arXiv  Detail & Related papers  (2021-05-11T09:54:20Z)
• The 5th AI City Challenge [51.83023045451549]
  The fifth AI City Challenge attracted 305 participating teams across 38 countries. The evaluation was conducted on both algorithmic effectiveness and computational efficiency. Results show the promise of AI in Smarter Transportation.
  arXiv  Detail & Related papers  (2021-04-25T19:15:27Z)
• The 4th AI City Challenge [80.00140907239279]
  The 4th annual edition of the AI City Challenge has attracted 315 participating teams across 37 countries. The evaluation is conducted on both algorithmic effectiveness and computational efficiency. Results show promise that AI technology can enable smarter and safer transportation systems.
  arXiv  Detail & Related papers  (2020-04-30T07:47:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.