19 citations · 19 across the 7 of their papers we have counts for
12 papers
Physical AI Smart Spaces: A Large-Scale Benchmark for Multi-Camera 3D Perception in Smart Spaces
Yuxing Wang, Yizhou Wang, Anqi Li +13
Physical AI Smart Spaces is, to the best of our knowledge, the first benchmark to simultaneously provide large-scale, multi-class, and multi-camera 3D perception data for indoor sm…
The 10th AI City Challenge
Zheng Tang, Shuo Wang, David C. Anastasiu +34
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 v…
SG-CADVLM: A Context-Aware Decoding Powered Vision Language Model for Safety-Critical Scenario Generation
Hongyi Zhao, Shuo Wang, Qijie He +1
Autonomous Vehicle (AV) requires rigorous testing in safety-critical scenarios for safety validation, yet its validation is hindered by the high cost of field testing and the lack…
The 9th AI City Challenge
Zheng Tang, Shuo Wang, David C. Anastasiu +25
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 fea…
The 8th AI City Challenge
Shuo Wang, David C. Anastasiu, Zheng Tang +21
The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (I…
The 7th AI City Challenge
Milind Naphade, Shuo Wang, David C. Anastasiu +17
The AI City Challenge's seventh edition emphasizes two domains at the intersection of computer vision and artificial intelligence - retail business and Intelligent Traffic Systems…
The 6th AI City Challenge
Milind Naphade, Shuo Wang, David C. Anastasiu +14
The 6th edition of the AI City Challenge specifically focuses on problems in two domains where there is tremendous unlocked potential at the intersection of computer vision and art…
Synthetic Distracted Driving (SynDD2) dataset for analyzing distracted behaviors and various gaze zones of a driver
Mohammed Shaiqur Rahman, Jiyang Wang, Senem Velipasalar Gursoy +3
This article presents a synthetic distracted driving (SynDD2 - a continuum of SynDD1) dataset for machine learning models to detect and analyze drivers' various distracted behavior…
The 5th AI City Challenge
Milind Naphade, Shuo Wang, David C. Anastasiu +11
The AI City Challenge was created with two goals in mind: (1) pushing the boundaries of research and development in intelligent video analysis for smarter cities use cases, and (2)…
PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data
Zheng Tang, Milind Naphade, Stan Birchfield +5
In comparison with person re-identification (ReID), which has been widely studied in the research community, vehicle ReID has received less attention. Vehicle ReID is challenging d…
The 4th AI City Challenge
Milind Naphade, Shuo Wang, David Anastasiu +7
The AI City Challenge was created to accelerate intelligent video analysis that helps make cities smarter and safer. Transportation is one of the largest segments that can benefit…
CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification
Zheng Tang, Milind Naphade, Ming-Yu Liu +6
Urban traffic optimization using traffic cameras as sensors is driving the need to advance state-of-the-art multi-target multi-camera (MTMC) tracking. This work introduces CityFlow…