论文标题
ESA-REID：基于熵的语义特征对齐的人Re-ID
ESA-ReID: Entropy-Based Semantic Feature Alignment for Person re-ID
论文作者
论文摘要
人重新识别（RE-ID）是现实世界中一项艰巨的任务。除了监视系统中的典型应用外，Re-ID还具有重要的价值，可以提高内容视频（电视或电影）中的人身份证的召回率。但是，遮挡，射击角度变化和复杂的背景使其远离应用程序，尤其是在内容视频中。在本文中，我们提出了一个基于熵的语义特征对齐模型，该模型占据了人类语义特征的详细信息。考虑到语义分割的不确定性，我们引入了具有基于熵掩模的语义对齐，可以减少掩盖分割误差的负面影响。我们根据内容视频构建一个新的重新ID数据集，其中有许多遮挡和身体部位丢失的情况，这将来会在以后发布。对现有数据集和新数据集的广泛研究表明了所提出的模型的出色性能。
Person re-identification (re-ID) is a challenging task in real-world. Besides the typical application in surveillance system, re-ID also has significant values to improve the recall rate of people identification in content video (TV or Movies). However, the occlusion, shot angle variations and complicated background make it far away from application, especially in content video. In this paper we propose an entropy based semantic feature alignment model, which takes advantages of the detailed information of the human semantic feature. Considering the uncertainty of semantic segmentation, we introduce a semantic alignment with an entropy-based mask which can reduce the negative effects of mask segmentation errors. We construct a new re-ID dataset based on content videos with many cases of occlusion and body part missing, which will be released in future. Extensive studies on both existing datasets and the new dataset demonstrate the superior performance of the proposed model.
