graviti
Products
Resources
About us
VeRI-Wild
2D Box
Vehicle
|...
License: Unknown

Overview

A Large Dataset and a New Method for Vehicle Re-Identification in the Wild.

Data Collection

A large-scale vehicle ReID dataset in the wild (VERI-Wild) is captured from a large CCTV surveillance
system consisting of 174 cameras across one month (30*24h) under unconstrained scenarios. The
cameras are distributed in a large urban district of more than 200km2. The YOLO-v2 [2] is used
to detect the bounding box of vehicles. The raw vehicle image set contains 12 million vehicle
images, and 11 volunteers are invited to clean the dataset for 1 month. After data cleaning
and annotation, 416,314 vehicle images of 40,671 identities are collected. The statistics of
VERI-Wild is illustrated in Figure. For privacy issues, the license plates are masked in the
dataset. The distinctive features of VERI-Wild are summarized into the following aspects:

Unconstrained capture conditions in the wild The VERI-Wild dataset is collected from a real
CCTV camera system consisting of 174 surveillance cameras, in which the unconstrained image
capture conditions pose a variety of challenges.
Complex capture conditions The 174 surveillance
cameras are distributed in an urban district over 200km2, presenting various backgrounds, resolutions,
viewpoints, and occlusion in the wild. In extreme cases, one vehicle appears in more than 40
different cameras, which would be challenging for ReID algorithms.
Large time span involving
severe illumination and weather changes The VERI-Wild is collected from a duration of 125,
280 (174x24x30) video hours.Figure (b)
gives the vehicle distributions in 4 time slots of 24h, i.e., morning, noon, afternoon, evening
across 30 days. VERI-Wild also contains poor weather conditions, such as rainy, foggy, etc,
which are not provided in previous datasets.
Rich Context Information We provide rich context
information such as camera IDs, timestamp, tracks relation across cameras, which are potential
to facilitate the research on behavior analysis in camera networks, like vehicle behavior modeling,
cross-camera tracking and graph-based retrieval.

Citation

Please use the following citation when referencing the dataset:

@inproceedings{lou2019large,
 title={VERI-Wild: A Large Dataset and a New Method for Vehicle Re-Identification in the Wild},
 author={Lou, Yihang and Bai, Yan and Liu, Jun and Wang, Shiqi and Duan, Ling-Yu},
 booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
 pages = {3235--3243},
 year={2019}
}
Data Summary
Type
Image,
Amount
--
Size
2.66GB
Provided by
Peking University
Peking University , is a major research university in Beijing, China, and a member of the elite C9 League of Chinese universities. It is perennially ranked as one of the top academic institutions in China, Asia, and worldwide.
| Amount -- | Size 2.66GB
VeRI-Wild
2D Box
Vehicle
License: Unknown

Overview

A Large Dataset and a New Method for Vehicle Re-Identification in the Wild.

Data Collection

A large-scale vehicle ReID dataset in the wild (VERI-Wild) is captured from a large CCTV surveillance
system consisting of 174 cameras across one month (30*24h) under unconstrained scenarios. The
cameras are distributed in a large urban district of more than 200km2. The YOLO-v2 [2] is used
to detect the bounding box of vehicles. The raw vehicle image set contains 12 million vehicle
images, and 11 volunteers are invited to clean the dataset for 1 month. After data cleaning
and annotation, 416,314 vehicle images of 40,671 identities are collected. The statistics of
VERI-Wild is illustrated in Figure. For privacy issues, the license plates are masked in the
dataset. The distinctive features of VERI-Wild are summarized into the following aspects:

Unconstrained capture conditions in the wild The VERI-Wild dataset is collected from a real
CCTV camera system consisting of 174 surveillance cameras, in which the unconstrained image
capture conditions pose a variety of challenges.
Complex capture conditions The 174 surveillance
cameras are distributed in an urban district over 200km2, presenting various backgrounds, resolutions,
viewpoints, and occlusion in the wild. In extreme cases, one vehicle appears in more than 40
different cameras, which would be challenging for ReID algorithms.
Large time span involving
severe illumination and weather changes The VERI-Wild is collected from a duration of 125,
280 (174x24x30) video hours.Figure (b)
gives the vehicle distributions in 4 time slots of 24h, i.e., morning, noon, afternoon, evening
across 30 days. VERI-Wild also contains poor weather conditions, such as rainy, foggy, etc,
which are not provided in previous datasets.
Rich Context Information We provide rich context
information such as camera IDs, timestamp, tracks relation across cameras, which are potential
to facilitate the research on behavior analysis in camera networks, like vehicle behavior modeling,
cross-camera tracking and graph-based retrieval.

Citation

Please use the following citation when referencing the dataset:

@inproceedings{lou2019large,
 title={VERI-Wild: A Large Dataset and a New Method for Vehicle Re-Identification in the Wild},
 author={Lou, Yihang and Bai, Yan and Liu, Jun and Wang, Shiqi and Duan, Ling-Yu},
 booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
 pages = {3235--3243},
 year={2019}
}
0
Start building your AI now
graviti
wechat-QR
Long pressing the QR code to follow wechat official account

Copyright@Graviti