The challenge of person re-identification (re-id) is to match individual images of the same person captured by different nonoverlapping camera views against significant and unknown cross-view feature distortion. While a large number of distance metric/ subspace learning models have been developed for re-id, the cross-view transformations they learned are view-generic and thus potentially less effective in quantifying the feature distortion inherent to each camera view. Learning view-specific feature transformations for re-id (i.e., view-specific re-id), an under-studied approach, becomes an alternative resort for this problem. In this work, we formulate a novel view-specific person re-identification framework from the feature augmentation point of view, called Camera coRrelation Aware Feature augmenTation (CRAFT). Specifically, CRAFT performs cross-view adaptation by automatically measuring camera correlation from cross-view visual data distribution and adaptively conducting feature augmentation to transform the original features into a new adaptive space. Through our augmentation framework, view-generic learning algorithms can be readily generalized to learn and optimize view-specific sub-models whilst simultaneously modelling view-generic discrimination information. Therefore, our framework not only inherits the strength of view-generic model learning but also provides an effective way to take into account view specific characteristics. Our CRAFT framework can be extended to jointly learn view-specific feature transformations for person re-id across a large network with more than two cameras, a largely under-investigated but realistic re-id setting. Additionally, we present a domain-generic deep person appearance representation which is designed particularly to be towards view invariant for facilitating cross-view adaptation by CRAFT. We conducted extensively comparative experiments to validate the superiority and advantages of our proposed framework over state-of...

中文翻译：

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通过相机相关感知特征增强进行人员重新识别


人员重新识别 (re-id) 的挑战是将不同非重叠摄像机视图捕获的同一个人的各个图像与显着且未知的交叉视图特征失真进行匹配。虽然已经开发了大量用于重新识别的距离度量/子空间学习模型，但他们学到的跨视图变换是视图通用的，因此在量化每个摄像机视图固有的特征失真方面可能不太有效。学习特定于视图的特征转换以进行重识别（即特定于视图的重识别）是一种尚未得到充分研究的方法，成为解决此问题的另一种方法。在这项工作中，我们从特征增强的角度制定了一种新颖的特定于视图的人物重新识别框架，称为相机相关感知特征增强（CRAFT）。具体来说，CRAFT通过从跨视图视觉数据分布自动测量相机相关性并自适应地进行特征增强以将原始特征转换到新的自适应空间来执行跨视图自适应。通过我们的增强框架，视图通用学习算法可以很容易地推广到学习和优化视图特定的子模型，同时对视图通用判别信息进行建模。因此，我们的框架不仅继承了视图通用模型学习的优势，而且提供了一种有效的方法来考虑视图的特定特征。我们的 CRAFT 框架可以扩展，以共同学习特定于视图的特征转换，以便在具有两个以上摄像头的大型网络中进行行人重新识别，这是一个很大程度上尚未得到充分研究但现实的重新识别设置。 此外，我们提出了一种领域通用的深层人物外观表示，其专门设计用于视图不变，以促进 CRAFT 的跨视图适应。我们进行了广泛的比较实验，以验证我们提出的框架相对于现有框架的优越性和优势......