49 citations · 91 across the 5 of their papers we have counts for
6 papers
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…
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…
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…
MOANA: An Online Learned Adaptive Appearance Model for Robust Multiple Object Tracking in 3D
Zheng Tang, Jenq-Neng Hwang
Multiple object tracking has been a challenging field, mainly due to noisy detection sets and identity switch caused by occlusion and similar appearance among nearby targets. Previ…
Multiple-Kernel Based Vehicle Tracking Using 3D Deformable Model and Camera Self-Calibration
Zheng Tang, Gaoang Wang, Tao Liu +5
Tracking of multiple objects is an important application in AI City geared towards solving salient problems related to safety and congestion in an urban environment. Frequent occlu…