Person Re-identification by Deep Learning Attribute-Complementary Information
Person Re-identification by Deep Learning Attribute-Complementary Information
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DOI：
10.1109/cvprw.2017.186
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发表时间：
2017-07
期刊：
影响因子：
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通讯作者：
Arne Schumann;R. Stiefelhagen
中科院分区：
文献类型：
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作者：
Arne Schumann;R. Stiefelhagen
跨摄像机边界的自动人员重新识别（re-id）是一个具有挑战性的问题。这些方法必须能够抵御许多影响人的视觉外观但与人的身份无关的因素。这些因素的示例是姿势、相机角度和照明条件。人的属性是一个语义上的高级信息，它在许多这样的影响下是不变的，并且包含的信息通常与人的身份高度相关。在这项工作中，我们开发了一个re-id的方法，利用自动检测的属性中包含的信息。我们在单独的数据上训练属性分类器，并将其响应包含到基于卷积神经网络（CNN）的person re-id模型的训练过程中。这使我们能够学习一个人的表示，其中包含的信息补充所包含的属性。我们的方法是能够识别的属性，执行最可靠的re-id和相应地专注于他们。我们展示了通过使用多个大规模数据集上的属性信息所获得的性能改进，并报告了哪些属性与人员re-id最相关的见解。
Automatic person re-identification (re-id) across camera boundaries is a challenging problem. Approaches have to be robust against many factors which influence the visual appearance of a person but are not relevant to the person's identity. Examples for such factors are pose, camera angles, and lighting conditions. Person attributes are a semantic high level information which is invariant across many such influences and contain information which is often highly relevant to a person's identity. In this work we develop a re-id approach which leverages the information contained in automatically detected attributes. We train an attribute classifier on separate data and include its responses into the training process of our person re-id model which is based on convolutional neural networks (CNNs). This allows us to learn a person representation which contains information complementary to that contained within the attributes. Our approach is able to identify attributes which perform most reliably for re-id and focus on them accordingly. We demonstrate the performance improvement gained through use of the attribute information on multiple large-scale datasets and report insights into which attributes are most relevant for person re-id.