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利用差异表示法进行人员重新识别

Exploiting Dissimilarity Representations for Person Re-Identification
课程网址: http://videolectures.net/simbad2011_satta_dissimilarity/  
主讲教师: Riccardo Satta
开课单位: 卡利亚里大学
开课时间: 信息不详。欢迎您在右侧留言补充。
课程语种: 英语
中文简介:
人员重新识别是识别已经通过视频监控摄像机网络观察到的个人的任务。迄今为止,文献中提出的方法将这个问题解决为经典匹配问题:直接从人的角度构建描述符,并相应地定义描述符之间的相似性度量。在这项工作中,我们提出了一个用于人员重新识别的通用相似框架,旨在将基于普遍采用的多实例表示的通用人员重新识别方法转换为相异形式。因此,相对于普通原型,通过相异值表示个体。差异表示具有吸引人的优势,特别是所得描述符的紧凑性以及匹配两个描述符所需的极短时间。此外,差异表示可以实现各种新应用,其中一些在本文中进行了描述。提供了对适用于现有方法的拟议框架的实验评估,该评估清楚地表明了在人员重新识别的背景下,不同表示的优势。
课程简介: Person re-identification is the task of recognizing an individual that has already been observed over a network of video-surveillance cameras. Methods proposed in literature so far addressed this issue as a classical matching problem: a descriptor is built directly from the view of the person, and a similarity measure between descriptors is defined accordingly. In this work, we propose a general dissimilarity framework for person re-identification, aimed at transposing a generic method for person re-identification based to the commonly adopted multiple instance representation, into a dissimilarity form. Individuals are thus represented by means of dissimilarity values, in respect to common prototypes. Dissimilarity representations carry appealing advantages, in particular the compactness of the resulting descriptor, and the extremely low time required to match two descriptors. Moreover, a dissimilarity representation enables various new applications, some of which are depicted in the paper. An experimental evaluation of the proposed framework applied to an existing method is provided, which clearly shows the advantages of dissimilarity representations in the context of person re-identification.
关 键 词: 匹配问题; 相似性; 人员重新识别
课程来源: 视频讲座网
最后编审: 2019-09-21:cwx
阅读次数: 41