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Key Point and Vehicle Orientation Annotation for VeRi-776 dataset

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Introduction

VeRi-776 is a large-scale benchmark dateset for vehicle Re-Id in the real-world urban surveillance scenario. It contains over 50,000 images of 776 vehicles captured by 20 cameras covering an 1.0 km^2 area in 24 hours, which makes the dataset scalable enough for vehicle Re-Id and other related research.

This repo has annotations of key point location and vehicle orientation for VeRi-776 dataset, which is used in our ICCV'17 paper Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification.

Get VeRi-776 dataset

Please refer to this repo.

Key points' definition

We defined 20 key points in a vehicle, which we think are the most discriminative locations or some main vehicle components, for instance, wheels, lamps, auto logo and so on. You can find our definition of the 20 key points in the figure and the table bellow.

definetion

index location index location
1 left-front wheel 11 left rear-view mirror
2 left-back wheel 12 right rear-view mirror
3 right-front whee l 13 right-front corner of vehicle top
4 right-back wheel 14 left-front corner of vehicle top
5 right fog lamp 15 left-back corner of vehicle top
6 left fog lamp 16 right-back corner of vehicle top
7 right headlight 17 left rear lamp
8 left headlight 18 right rear lamp
9 front auto logo 19 rear auto logo
10 front license plate 20 rear license plate

Orientation's definition

We classify the orientation of a vehicle into 8 categories, according to which face(s) of the vehicle is visible in this view :

0 front
1 rear
2 left
3 left front
4 left rear
5 right
6 right front
7 right rear

Annotation file format

In each line in the annotation file, the format is:

img_path x1 y1 x2 y2 ... x20 y20 orien

(x_i,y_i) is the location of the ith key point of a vehicle, and orien is the orientation label.

Citation

If you find this repo useful in your research, please consider to cite:

@InProceedings{Wang_2017_ICCV,
	author = {Wang, Zhongdao and Tang, Luming and Liu, Xihui and Yao, Zhuliang and Yi, Shuai and Shao, Jing and Yan, Junjie and Wang, Shengjin and Li, Hongsheng and Wang, Xiaogang},
	title = {Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-Identification},
	booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
	month = {Oct},
	year = {2017}
}

Contact

Please concact Zhongdao Wang (wcd17@mails.tsinghua.edu.cn) if you have questions about the annotations.