Fast Person Re-identification via Cross-Camera Semantic Binary Transformation
Fast Person Re-identification via Cross-Camera Semantic Binary Transformation
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DOI：
10.1109/cvpr.2017.566
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发表时间：
2017-07
期刊：
影响因子：
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通讯作者：
Jiaxin Chen;Yunhong Wang;Jie Qin;Li Liu;Ling Shao
中科院分区：
文献类型：
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作者：
Jiaxin Chen;Yunhong Wang;Jie Qin;Li Liu;Ling Shao
已有很多人重新识别的方法被提出，但是大多数方法忽略了匹配效率。最近，已经开发了几种基于散列的方法来使大规模画廊集的重新识别更具可扩展性。尽管效率很高，但这些工作忽略了摄像机之间的差异，这严重恶化了最终的匹配精度。针对上述问题，我们提出了一种新的基于哈希的快速身份识别方法，即跨摄像机语义二进制变换(CSBT)。CSBT的目标是将原始的高维特征向量转换为紧凑的保持身份的二进制代码。为此，CSBT首先使用子空间投影，通过最大化人内相似性和人与人之间的差异来减少跨相机差异。随后，通过无缝结合语义成对关系和局部亲和力信息，提出了一种二进制编码方案。最后，提出了一种基于离散交替优化的同时进行子空间投影学习和二进制编码的联合学习框架。在四个基准上的实验结果清楚地表明了CSBT方法相对于最新方法的优越性。
Numerous methods have been proposed for person re-identification, most of which however neglect the matching efficiency. Recently, several hashing based approaches have been developed to make re-identification more scalable for large-scale gallery sets. Despite their efficiency, these works ignore cross-camera variations, which severely deteriorate the final matching accuracy. To address the above issues, we propose a novel hashing based method for fast person re-identification, namely Cross-camera Semantic Binary Transformation (CSBT). CSBT aims to transform original high-dimensional feature vectors into compact identity-preserving binary codes. To this end, CSBT first employs a subspace projection to mitigate cross-camera variations, by maximizing intra-person similarities and inter-person discrepancies. Subsequently, a binary coding scheme is proposed via seamlessly incorporating both the semantic pairwise relationships and local affinity information. Finally, a joint learning framework is proposed for simultaneous subspace projection learning and binary coding based on discrete alternating optimization. Experimental results on four benchmarks clearly demonstrate the superiority of CSBT over the state-of-the-art methods.