用颜色名称改进局部最大出现率的人物再识别

Mengye Song, Shengrong Gong, Chunping Liu, Yi Ji, Husheng Dong
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引用次数: 4

摘要

人员再识别是将跨摄像机的人员与非重叠视场关联起来的任务。人物再识别的两个关键方面是特征表示和度量学习。所采用的特征表示既要具有判别性又要具有不变性,这也是本文所考虑的问题。为了提高人的再识别性能,我们提出将改进的局部最大出现描述符(LOMO)与语义颜色名称(SCN)相结合。特别地,我们在LOMO中引入了人体的对称信息来抑制背景的影响。当与中级基于属性的描述-语义颜色名称融合时,我们得到了更有鉴别性的特征。基于KISS度量,对具有挑战性的VIPeR数据集进行了评估,结果表明该方法显著提高了再识别效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Person re-identification by improved Local Maximal Occurrence with color names
Person re-identification is the task of associating people across cameras with non-overlapping view field. Two key aspects of Person re-identification are the feature representation and metric learning. The feature representation employed should be both discriminative and invariant, which is also our considering in this paper. To enhance person re-identification performance, we propose to combine improved Local Maximal Occurrence (LOMO) descriptor with semantic color names (SCN). Especially, we introduce symmetry information of human body to suppress the impact of background in LOMO. When fused with mid-level attribute-based description - sematic color names, our more discriminative signature is obtained. Based on the KISS metric, evaluation on the challenging VIPeR dataset shows that the proposed method improves the re-identification significantly.
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