行人重识别目标中心编码外观模型的研究

项目名称： 行人重识别目标中心编码外观模型的研究

项目编号： No.61501177

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 无线电电子学、电信技术

项目作者： 杨钊

作者单位： 广州大学

项目金额： 19万元

中文摘要： 行人重识别是计算机视觉领域中具有挑战性的问题，主要表现在不同摄像头下的行人在视角、姿态、光照上具有较大的差异以及目标区域容易受背景的干扰。因此为目标行人建立良好的外观模型对提高行人重识别的准确性具有重要的意义。针对目前传统外观模型方法过多依赖于复杂视觉特征的组合且识别率不高的情况。本项目提出目标中心编码的外观模型以进一步提高行人重识别的准确性。主要研究内容包括：（1）研究目标行人前景区域的提取策略，以解决行人目标“不对齐”现象和减少背景的干扰。（2）在前景目标区域提取的基础上，提出使用目标中心编码的方法以提高行人重识别的准确性。（3）针对目前对外观模型评价方法单一的问题，提出使用多种距离学习的方法来评价外观模型的性能。本项目的研究为提高行人重识别准确性提供了新的思路。

中文关键词： 行人重识别；前景提取；特征表达；局部约束编码

英文摘要： Person re-identification is a challenging task in computer vision due to the significant variations on viewpoints, poses and illuminations among different cameras. In addition, background clutters and occlusions increase the difficulties. Therefore, it is critical important to design good performance appearance model for improving the accuracy of person re-identification. However, the traditional appearance models rely on excessive vision features in complicated styles and the accuracy of person re-identification is not very high. With this motivation, in this project we present a new Object Centric Coding (OCC) appearance model for person re-identification. It includes the following aspects..First, the mask containing the genuine body is obtained for each person via a foreground extraction tragedy. The purpose of this phase is to separate the appearance of body parts from the rest of the scene, which enables the following coding descriptor to focus on the sole person..Second, by removing the background region, discriminant vision features are learnt followed by Locality-constrained Linear Coding (LLC), which could significantly improve the performance of low level features..Third, to obtain a proper evaluation for the OCC, we validate the OCC model on the challenging public datasets using multi metric learning methods. We believe that a good appearance model is likely to be validated by different metrics and produces consistent and high performance..Finally, this project aims to design a robust and discriminant appearance model for boosting the accuracy (matching rates) in person re-identifications. Also it presents a new solution for how to improve the performance of person re-identification.

英文关键词： Person re-identification;Foreground extraction;Feature representation;Locality-constrained coding