SAIVT Soft Biometric Database (SAIVT-SoftBio)

SAIVT Soft Biometric Database

Overview

The SAIVT-SoftBio database contains a collection of multi-camera sequences of 152 pedestrians captured from a set of 8 surveillance cameras. This database provides a challenging and realistic test-bed for person redetection tasks, and is freely available for download. Contact Dr Simon Denman (s dot denman at qut dot edu dot au) for more information.

Licensing

The SAIVT-SOFTBIO database is © 2012 QUT and is licensed under the Creative Commons Attribution-ShareAlike 3.0 Australia License (http://creativecommons.org/licenses/by-sa/3.0/).

Attribution

To attribute this database, use the citation provided on our publication at [eprints] (http://eprints.qut.edu.au/53437/).

Acknowledgement in publications

In addition to citing our paper, we request the following text be included in your publications:
'We would like to thank the SAIVT Research Labs at Queensland University of Technology (QUT) for freely supplying us with the SAIVT-SoftBio database for our research'.

Installing the SAIVT-SoftBio Database

Download, join, and unpack the four files (and optionally the motion segmentation masks).

Once the main archive has been uboacked, you should have the following data structure and the SAIVT-SoftBio database is installed:

​SAIVT-SoftBio
+-- Calibration
+-- C1--U1-17
+-- C2--U18-48
...
+-- C10--U140-152
+-- Uncontrolled
+-- Subject001
+-- Subject002
+-- Subject003
...
+-- Subject152
+-- Bialkowski2012 - A database for person re-identification in multi-camera surveillance networks.pdf
+-- LICENSE.txt
+-- README.txt
+-- SAIVTSoftBioDatabase.xml

The 'Calibration' directory contains a camera calibration and background images (one image per camera) for the dataset. It is arranged into groups of subjects (i.e. C1--U1-17 contains camera calibration and background images valid for subjects 1 to 17). All camera calibration has been calculated using Tsai's method.
The 'Uncontrolled' directory contains the image sequences for each subject, arranged by camera view.

The 'SAIVTSoftBioDatabase.xml' file defines the database. This file specifies the number of cameras used and number of calibrations present, the regions of interest for each camera ( tags), the location of the calibration information ( tags), and the subjects themselves ( tags). Note that for each subject, a camera calibration is specified.

More information on the SAIVT-SOFTBIO database in our paper at QUT eprints (http://eprints.qut.edu.au/53437/)

SAIVT软生物特征数据库（SAIVT Soft Biometric Database）

## 概述
SAIVT-SoftBio数据库包含由8台监控摄像头采集的152名行人的多摄像头序列数据集。该数据集为行人重识别（Person Re-identification）任务提供了兼具挑战性与真实性的测试基准平台，且可免费下载获取。如需获取更多信息，请联系Simon Denman博士，邮箱地址：[e-mail]。

## 许可声明
SAIVT-SOFTBIO数据库版权归2012年昆士兰理工大学（Queensland University of Technology, QUT）所有，采用知识共享署名-相同方式共享3.0澳大利亚许可协议（Creative Commons Attribution-ShareAlike 3.0 Australia License），具体许可条款可访问：http://creativecommons.org/licenses/by-sa/3.0/。

## 署名规范
引用该数据集时，请使用我们在eprints平台发布的论文中的引用格式，链接地址：http://eprints.qut.edu.au/53437/。

## 出版物致谢
除引用相关论文外，恳请在您的出版物中加入以下致谢文本：
'我们感谢昆士兰理工大学（QUT）SAIVT研究实验室为我们的研究免费提供SAIVT-SoftBio数据库。'

## SAIVT-SoftBio数据库安装指南
下载、合并并解压四个压缩包文件（可选附带运动分割掩码文件）。主归档文件解压完成后，您将获得如下数据目录结构，此时SAIVT-SoftBio数据库即安装完成：

SAIVT-SoftBio
+-- Calibration
+-- C1--U1-17
+-- C2--U18-48
...
+-- C10--U140-152
+-- Uncontrolled
+-- Subject001
+-- Subject002
+-- Subject003
...
+-- Subject152
+-- Bialkowski2012 - A database for person re-identification in multi-camera surveillance networks.pdf
+-- LICENSE.txt
+-- README.txt
+-- SAIVTSoftBioDatabase.xml

- **Calibration目录**：包含本数据集的摄像头标定数据与背景图像（每台摄像头对应一张背景图），该目录按受试者分组进行组织（例如`C1--U1-17`包含适用于受试者1至17的摄像头标定与背景图像）。所有摄像头标定均采用蔡氏标定法（Tsai's Method）完成。
- **Uncontrolled目录**：包含每位受试者的图像序列，按摄像头视角进行分类存储。
- **SAIVTSoftBioDatabase.xml文件**：用于定义该数据库，该文件指定了所用摄像头数量、标定文件总数、各摄像头的感兴趣区域（对应标签）、标定信息的存储位置（对应标签）以及受试者信息（对应标签）。请注意，每位受试者均绑定指定的摄像头标定参数。

更多关于SAIVT-SOFTBIO数据库的详细信息，请参阅我们发布于QUT eprints平台的论文，链接地址：http://eprints.qut.edu.au/53437/

搜集汇总

背景与挑战

背景概述

SAIVT-SoftBio数据库是一个用于人员重检测研究的监控数据集，包含152名行人在8个摄像头下的多视角图像序列，提供真实且具有挑战性的测试环境。该数据集遵循Creative Commons Attribution-ShareAlike 3.0许可，可免费获取，适用于计算机视觉和生物识别领域的研究。