The 9th AI City Challenge

TL;DR

The 9th AI City Challenge showcased advancements in computer vision and AI across transportation, automation, and safety, with increased participation and new datasets fostering innovation in multi-camera tracking, incident understanding, spatial reasoning, and real-time detection.

Contribution

This edition introduced diverse tracks with novel datasets and benchmarks, promoting progress in multi-camera tracking, incident analysis, spatial reasoning, and edge-efficient detection in urban environments.

Findings

Achieved new state-of-the-art results in multiple tracks.

Generated extensive datasets with over 30,000 downloads.

Enhanced benchmarks for real-world AI applications in city environments.

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.