AI城市挑战赛9年聚焦真实场景，四赛道推动视觉算法落地。
The 9th AI City Challenge
- 四赛道并行，覆盖交通、仓储、安全等多场景视觉任务。
- 245支队伍来自15国，数据集下载超3万次，参与度提升17%。
- 强调轻量部署与可复现性，推动真实世界应用落地。
第九届AI城市挑战赛持续推动计算机视觉与人工智能在交通、工业自动化及公共安全领域的实际应用。2025年赛事设四个赛道，参与团队达245支，来自15个国家，较往年增长17%。挑战赛公开数据集至今下载量超3万次。第一赛道聚焦多类3D多摄像头跟踪，涵盖人、类人机器人、自主移动机器人和叉车，使用精细标定与3D边界框标注。第二赛道解决交通安全隐患的视频问答，结合3D注视标签增强多摄像头事件理解。第三赛道针对动态仓库环境中的细粒度空间推理，要求系统基于RGB-D输入回答融合感知、几何与语言的空间问题，数据集由NVIDIA Omniverse生成。第四赛道关注鱼眼相机下的高效道路目标检测，支持边缘设备的轻量化实时部署。评估框架限制提交次数，并采用部分保留测试集以确保公平性。最终排名在比赛结束后公布，促进结果可复现并减少过拟合。多个团队取得顶尖表现，在多项任务中设立新基准。
原文摘要 · Abstract (English)
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.
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