Authors: Zheng Tang, Shuo Wang, David C. Anastasiu, Ming-Ching Chang, Anuj Sharma, Quan Kong, Munkhjargal Gochoo, Jun-Wei Hsieh, Tomasz Kornuta, Zhedong Zheng, Renran Tian, Judah Goldfeder, Fulgencio Navarro, Yuxing Wang, Yizhou Wang, Sameer Satish Pusegaonkar, Anqi Li, Nalin Dadhich, Ridham Kachhadiya, Dhanishtha Patil, Haoquan Liang, Jiajun Li, Han Zhang, Yilin Zhao, Zaid Pervaiz Bhat, Shuyu Yang, Ashutosh Kumar, Rong Wang, Rafael Martin Nieto, Peter Christiansen, Ahmed Abduljawad, Mohanrasu Shanmugam, Nadeem Shaik, Sujit Biswas, Xunlei Wu, Vidya Murali, Rama Chellappa
Organizations: NVIDIA · Santa Clara University · University at Albany, SUNY · Iowa State University · Woven by Toyota · United Arab Emirates University · National Yang Ming Chiao Tung University · North Carolina State University · Columbia University · Milestone Systems · Xi’an Jiaotong University · Johns Hopkins University
The 10th AI City Challenge, held with ECCV 2026, marks a decade of community benchmarking for intelligent transportation, smart cities, and physical AI. Since its 2017 start with vehicle detection, classification, and tracking, the challenge has grown into a broad benchmark suite for multi-camera perception, multimodal reasoning, synthetic-to-real learning, generative forecasting, and privacy-preserving evaluation. The 2026 edition continued this growth with 325 registered teams, up from 245 in 2025, and participation from 26 countries and regions, up from 15. Its six primary tracks cover multi-camera 3D perception, transportation safety captioning and VQA, traffic anomaly reasoning, text-based person anomaly search, generative traffic video forecasting, and cross-city object detection. Track 3 further includes two out-of-domain leaderboards, submitted as Tracks 7 and 8, for fisheye traffic-violation understanding and pedestrian situated-intent VQA. This paper summarizes the challenge setup, datasets, evaluation protocols, leaderboard results, and workshop papers. Across tracks, successful systems combine foundation models with geometric grounding, retrieval or reranking, synthetic-data design, domain adaptation, and controlled inference.
Figures & tables
Figure 1: Track 1 sample: synchronized multi-camera warehouse views and a top-down layout for RGB-only multi-camera 3D perception.
Figure 2: Track 2 sample: synthetic Digital Twin WTS scenes paired with real WTS-style views and structured pedestrian/vehicle descriptions.
Figure 3: Track 3 sample: traffic anomaly reasoning combines video evidence, task-specific questions, event descriptions, and causal explanations.
Figure 4: Track 4 sample: synthetic PAB training examples and real-gallery retrieval, including hard negatives that share appearance or scene context with the query.
Figure 5: Track 5 sample: future-frame generation from history frames and target behavior descriptions in traffic scenes.
Figure 6: Track 6 sample: privacy-preserved traffic-camera images from Milestone Systems (Project Hafnia), with annotated source-city frames on top and hidden target-city frames below.
Rank
Team ID
Team
3D HOTA
Paper
1/15 (2/30)
289
EVA
56.5447
[ 66 ] ; online=true
2/15 (3/30)
34
SKKU-AL-T1
52.0118
[ 55 ] ; online=true
3/15 (4/30)
130
Playbox
38.0105
[ 53 ] ; online=true
4/15 (5/30)
4
QDTers
34.1845
[ 29 ] ; online=true
5/15 (6/30)
133
TU-YMLab
25.9712
[ 21 ] ; online=true
Table 1: Track 1 leaderboard entries connected to accepted papers.
Rank
Team ID
Team
S2
Paper
1/22 (1/44)
47
Latent Painter - UTE
60.0853
[ 11 ] ; VL-JEPA
2/22 (2/44)
24
UIT - Kitchen
57.3307
[ 38 ] ; VQA
3/22 (4/44)
266
KZ6
56.7949
[ 23 ] ; Qwen-3-VL-8B
8/22 (14/44)
127
Team KODE
55.4679
[ 27 ] ; Qwen
Table 2: Track 2 leaderboard entries connected to accepted papers.
Rank
Team ID
Team
Mean
Paper
1/27 (1/76)
25
Stellarview AI
0.6788
[ 26 ] ; Qwen 3.5
- (2/76)
45
UOB&UW Team
0.6779
[ 34 ] ; Qwen3VL-8B
2/27 (5/76)
60
FPT AI Vision
0.6703
[ 20 ] ; Qwen3-VL-8B
3/27 (7/76)
12
Smart Vision
0.6669
[ 57 ] ; Qwen
10/27 (26/76)
30
UWIPL_ETRI
0.6185
[ 52 ] ; Qwen
15/27 (36/76)
122
OptimAI
0.5880
[ 17 ] ; Qwen
Table 3: Track 3 leaderboard entries connected to accepted papers.
Rank
Team ID
Team
mAP
Paper
1/28 (3/47)
59
Xiilab.AIpex
99.3020
[ 47 ]
3/28 (6/47)
9
hiensumi
98.3535
[ 15 ]
7/28 (9/47)
27
VGU AI LAB
95.4078
[ 49 ]
8/28 (11/47)
97
SMART Lab
94.7815
[ 13 ]
11/28 (14/47)
76
HCMUS_4CentralVN
93.6715
[ 61 ]
13/28 (15/47)
93
SelabHuman
93.6577
[ 41 ]
Table 4: Track 4 leaderboard entries connected to accepted papers.
Rank
Team ID
Team
Final
Paper
1/12 (2/18)
209
Qyn
76.4866
[ 14 ]
2/12 (3/18)
78
SSUPER
76.0385
[ 30 ]
3/12 (4/18)
47
Latent Painter - UTE
75.4302
[ 42 ]
4/12 (6/18)
83
CHTTL_A30
74.0544
[ 12 ]
5/12 (7/18)
39
VGU_ai_lab
73.3037
[ 28 ]
Table 5: Track 5 leaderboard entries connected to accepted papers.
Rank
Team ID
Team
mAP
Paper
1/25 (1/35)
34
SKKU-AL-T1
0.4753
[ 50 ]
2/25 (2/35)
265
BIT-ODL
0.4281
[ 24 ]
4/25 (5/35)
261
BK2TheFuture
0.4169
[ 51 ]
- (7/35)
315
lyx
0.4060
[ 58 ]
Table 6: Track 6 leaderboard entries connected to accepted papers.
School of Computer Science and Technology, University of Chinese Academy of Sciences (UCAS), Beijing, China · State Key Laboratory of AI Safety, Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS), Beijing, China · Beijing Academy of Artificial Intelligence (BAAI), Beijing, China +1