面向视频侦查的行人重识别关键技术研究

项目名称： 面向视频侦查的行人重识别关键技术研究

项目编号： No.61303114

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 梁超

作者单位： 武汉大学

项目金额： 26万元

中文摘要： 随着平安城市工程的深入推进，视频侦查逐渐成为公安部门打击和防控刑事犯罪的重要手段。作为其中的关键技术，行人重识别，即针对特定行人对象的视频检索，发挥着越来越重要的作用。现有的行人重识别方法主要分为两类：基于代表图特征描述的方法和基于距离学习的方法。前者通过抽取部分关键帧来表示行人图像序列。由于遗漏来大量视觉信息，因而匹配结果并不准确。后者的性能提升依赖大量标注样本，这在实际应用中往往难以有效获取。为此，本课题提出一整套基于模型表示的行人重识别方法：首先，对对行人对象的图像序列进行建模，获得行人外观的完整表示；其次，采用协同训练的方法，利用大量未标注样本来提升基于模型表示的距离学习算法的性能；最后，将双向验证的思想引入到行人重识别结果的重排序之中，进一步改善结果的准确率。本课题预期在标准公开数据集上对行人重识别的准确率提升近10%，从而显著提升视频侦查工作的效能。

中文关键词： 视频侦查；行人重识别；特征表示；距离度量；排序优化

英文摘要： With the deepening of the Safe City project, video investigation is gradually becoming an important mean for police to fight against, prevent and control criminal activities. As one of the key technologies of video investigation, person re-identification, namely searching specific person from mass surveillance videos, is becoming more and more important. Current person re-identification methods can be generally divided into two categories: key frame based representative image method and metric based distance learning method. The former usually selects a group of representative images to represent the person image sequence, and conducts distance comparison in the standard metric space. Due to the discard of lots of images captured in various cameras, such key frame based method can hard achieve ideal matching result. On the other side, the performance of distance learning based method heavily depends on the quantity of labeled samples. Since the acquisition of labeled samples is usually both label intensive and time consuming, the application scope of above supervised method is usually severely limited. This project presents a complete person re-identification method based on model representation and computation. First, we build a sequential object appearance model to represent the length-variable person image se

英文关键词： video investigation；person re-identification；feature representation；distance measurement；ranking optimization