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CityFlow
2D Box Tracking
Others
Urban
|...
License: Unknown

Overview

Data for this challenge comes from multiple traffic cameras from a city in the United States
as well as from state highways in Iowa. Specifically, we have time-synchronized video feeds
from several traffic cameras spanning major travel arteries of the city. Most of these feeds
are high resolution 1080p feeds at 10 frames per second. The vantage point of these cameras
is for traffic and transportation purposes and the data will be redacted in terms of faces
and license plates to address data privacy issues. Moreover, we have built a synthetic vehicle
data set consisting of over 1,300 distinct vehicles and over 140,000 images. These synthetic
images will form an augmented training set to be used along with the real-world traffic data
set for Tracks 2 and 3.

  • Urban Intersection and Highway Data for Vehicle Counting – About
    9 hours of videos captured from 20 different vantage points (including intersection single
    approaches, full intersections, highway segments and city streets) covering various lighting
    and weather conditions (including dawn, rain, and snow). Videos are 960p or better, and most
    have been captured at 10 frames per second.
  • Urban Intersection and Highway Data for Multi-Camera
    Vehicle Tracking – Nearly 3 hours of synchronized videos synchronously captured from multiple
    vantage points at various urban intersections and along highways. Videos are 960p or better,
    and most have been captured at 10 frames per second.
  • Synthetic Vehicle Data for Vehicle
    Re-ID – Over 190,000 images of over 1,300 distinct vehicles. These synthetic images will form
    an augmented training set to be used along with the real-world data for vehicle re-identification
    and multi-camera vehicle tracking tasks.
  • Iowa State University Data – More than 25 hours of video data captured on highways in Iowa.
  • Metadata about the collected videos, including
    GPS locations of cameras, camera calibration information and other derived data from videos.

Citation

Please cite the following papers accordingly if you choose to work with our datasets or refer
to the previous challenge results:

1.Vehicle MTMC dataset – CityFlow: A City-Scale Benchmark
for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification

@InProceedings{Tang_2019_CVPR,
author = {Zheng Tang and Milind Naphade and Ming-Yu Liu and Xiaodong Yang and Stan Birchfield
and Shuo Wang and Ratnesh Kumar and David Anastasiu and Jenq-Neng Hwang},

title = {CityFlow:
A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

2.Synthetic 3D vehicle dataset – Simulating content consistent vehicle datasets with attribute
descent

@InProceedings{Yao19VehicleX,
author={Yue Yao and Liang Zheng and Xiaodong Yang and Milind Naphade and Tom Gedeon},

title = {Simulating content consistent vehicle datasets with attribute descent},
howpublished = {arXiv:1912.08855},
year = {2019}
}

3.2020 challenge summary paper – The 4th AI City Challenge

@InProceedings{Naphade20AIC20,
author = {Milind Naphade and Shuo Wang and David
C. Anastasiu and Zheng Tang and Ming-Ching Chang and Xiaodong Yang and Liang Zheng and Anuj
Sharma and Rama Chellappa and Pranamesh Chakraborty}.

title = {The 4th AI City Challenge},

booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}

}

Data Summary
Type
Video, Image,
Amount
--
Size
35.72GB
Provided by
NVIDIA
Fueled by the massive growth of the gaming market and its insatiable demand for better 3D graphics, we've evolved the GPU into a computer brain at the intersection of virtual reality, high performance computing, and artificial intelligence. NVIDIA GPU computing has become the essential tool of the da Vincis and Einsteins of our time.
| Amount -- | Size 35.72GB
CityFlow
2D Box Tracking Others
Urban
License: Unknown

Overview

Data for this challenge comes from multiple traffic cameras from a city in the United States
as well as from state highways in Iowa. Specifically, we have time-synchronized video feeds
from several traffic cameras spanning major travel arteries of the city. Most of these feeds
are high resolution 1080p feeds at 10 frames per second. The vantage point of these cameras
is for traffic and transportation purposes and the data will be redacted in terms of faces
and license plates to address data privacy issues. Moreover, we have built a synthetic vehicle
data set consisting of over 1,300 distinct vehicles and over 140,000 images. These synthetic
images will form an augmented training set to be used along with the real-world traffic data
set for Tracks 2 and 3.

  • Urban Intersection and Highway Data for Vehicle Counting – About
    9 hours of videos captured from 20 different vantage points (including intersection single
    approaches, full intersections, highway segments and city streets) covering various lighting
    and weather conditions (including dawn, rain, and snow). Videos are 960p or better, and most
    have been captured at 10 frames per second.
  • Urban Intersection and Highway Data for Multi-Camera
    Vehicle Tracking – Nearly 3 hours of synchronized videos synchronously captured from multiple
    vantage points at various urban intersections and along highways. Videos are 960p or better,
    and most have been captured at 10 frames per second.
  • Synthetic Vehicle Data for Vehicle
    Re-ID – Over 190,000 images of over 1,300 distinct vehicles. These synthetic images will form
    an augmented training set to be used along with the real-world data for vehicle re-identification
    and multi-camera vehicle tracking tasks.
  • Iowa State University Data – More than 25 hours of video data captured on highways in Iowa.
  • Metadata about the collected videos, including
    GPS locations of cameras, camera calibration information and other derived data from videos.

Citation

Please cite the following papers accordingly if you choose to work with our datasets or refer
to the previous challenge results:

1.Vehicle MTMC dataset – CityFlow: A City-Scale Benchmark
for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification

@InProceedings{Tang_2019_CVPR,
author = {Zheng Tang and Milind Naphade and Ming-Yu Liu and Xiaodong Yang and Stan Birchfield
and Shuo Wang and Ratnesh Kumar and David Anastasiu and Jenq-Neng Hwang},

title = {CityFlow:
A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

2.Synthetic 3D vehicle dataset – Simulating content consistent vehicle datasets with attribute
descent

@InProceedings{Yao19VehicleX,
author={Yue Yao and Liang Zheng and Xiaodong Yang and Milind Naphade and Tom Gedeon},

title = {Simulating content consistent vehicle datasets with attribute descent},
howpublished = {arXiv:1912.08855},
year = {2019}
}

3.2020 challenge summary paper – The 4th AI City Challenge

@InProceedings{Naphade20AIC20,
author = {Milind Naphade and Shuo Wang and David
C. Anastasiu and Zheng Tang and Ming-Ching Chang and Xiaodong Yang and Liang Zheng and Anuj
Sharma and Rama Chellappa and Pranamesh Chakraborty}.

title = {The 4th AI City Challenge},

booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}

}

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