Abstract
Partial person re-identification (re-id) is a challenging problem, where only several partial observations (images) of people are available for matching. However, few studies have provided flexible solutions to identifying a person in an image containing arbitrary part of the body. In this paper, we propose a fast and accurate matching method to address this problem. The proposed method leverages Fully Convolutional Network (FCN) to generate fix-sized spatial feature maps such that pixel-level features are consistent. To match a pair of person images of different sizes, a novel method called Deep Spatial feature Reconstruction (DSR) is further developed to avoid explicit alignment. Specifically, DSR exploits the reconstructing error from popular dictionary learning models to calculate the similarity between different spatial feature maps. In that way, we expect that the proposed FCN can decrease the similarity of coupled images from different persons and increase that from the same person. Experimental results on two partial person datasets demonstrate the efficiency and effectiveness of the proposed method in comparison with several state-of-the-art partial person re-id approaches. Additionally, DSR achieves competitive results on a benchmark person dataset Market1501 with 83.58\% Rank-1 accuracy.
Abstract (translated)
部分人员重新识别（重新识别）是一个具有挑战性的问题，其中只有几个人的部分观察（图像）可用于匹配。然而，很少有研究提供灵活的解决方案来识别包含身体任意部分的图像中的人。在本文中，我们提出了一种快速准确的匹配方法来解决这个问题。所提出的方法利用完全卷积网络（FCN）来生成固定大小的空间特征映射，使得像素级特征是一致的。为了匹配一对不同大小的人物图像，进一步开发了一种称为深度空间特征重建（DSR）的新方法以避免显式对齐。具体地，DSR利用来自流行字典学习模型的重建误差来计算不同空间特征图之间的相似性。以这种方式，我们期望所提出的FCN可以降低来自不同人的耦合图像的相似性并且增加来自同一个人的相似性。两个部分人员数据集的实验结果证明了所提出的方法与几种最先进的部分人员重新识别方法的效率和有效性。此外，DSR在基准人数据集Market1501上获得了具有83.58 \％Rank-1准确度的竞争结果。
URL
https://arxiv.org/abs/1801.00881
