{"ID":2880551,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.13564","arxiv_id":"2508.13564","title":"The 9th AI City Challenge","abstract":"The ninth AI City Challenge continues to advance real-world applications of computer vision and AI in transportation, industrial automation, and public safety. The 2025 edition featured four tracks and saw a 17% increase in participation, with 245 teams from 15 countries registered on the evaluation server. Public release of challenge datasets led to over 30,000 downloads to date. Track 1 focused on multi-class 3D multi-camera tracking, involving people, humanoids, autonomous mobile robots, and forklifts, using detailed calibration and 3D bounding box annotations. Track 2 tackled video question answering in traffic safety, with multi-camera incident understanding enriched by 3D gaze labels. Track 3 addressed fine-grained spatial reasoning in dynamic warehouse environments, requiring AI systems to interpret RGB-D inputs and answer spatial questions that combine perception, geometry, and language. Both Track 1 and Track 3 datasets were generated in NVIDIA Omniverse. Track 4 emphasized efficient road object detection from fisheye cameras, supporting lightweight, real-time deployment on edge devices. The evaluation framework enforced submission limits and used a partially held-out test set to ensure fair benchmarking. Final rankings were revealed after the competition concluded, fostering reproducibility and mitigating overfitting. Several teams achieved top-tier results, setting new benchmarks in multiple tasks.","short_abstract":"The ninth AI City Challenge continues to advance real-world applications of computer vision and AI in transportation, industrial automation, and public safety. The 2025 edition featured four tracks and saw a 17% increase in participation, with 245 teams from 15 countries registered on the evaluation server. Public rele...","url_abs":"https://arxiv.org/abs/2508.13564","url_pdf":"https://arxiv.org/pdf/2508.13564v1","authors":"[\"Zheng Tang\",\"Shuo Wang\",\"David C. Anastasiu\",\"Ming-Ching Chang\",\"Anuj Sharma\",\"Quan Kong\",\"Norimasa Kobori\",\"Munkhjargal Gochoo\",\"Ganzorig Batnasan\",\"Munkh-Erdene Otgonbold\",\"Fady Alnajjar\",\"Jun-Wei Hsieh\",\"Tomasz Kornuta\",\"Xiaolong Li\",\"Yilin Zhao\",\"Han Zhang\",\"Subhashree Radhakrishnan\",\"Arihant Jain\",\"Ratnesh Kumar\",\"Vidya N. Murali\",\"Yuxing Wang\",\"Sameer Satish Pusegaonkar\",\"Yizhou Wang\",\"Sujit Biswas\",\"Xunlei Wu\",\"Zhedong Zheng\",\"Pranamesh Chakraborty\",\"Rama Chellappa\"]","published":"2025-08-19T06:55:06Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.LG\",\"cs.RO\"]","methods":"[]","has_code":false}