Matching manually cropped pedestrian images between queries and candidates, termed as person re-identification, has achieved significant progress with deep convolutional neural networks. Recently, a topic called ‘person search’ is proposed for the end-to-end application of re-identification technologies. It integrates object detection and person re-identification and aims to both locate and match pedestrians on a gallery of raw images. However, the design and implementation of such kind of hybrid network are difficult and computationally consuming in real practical situations. In order to fasten the design and ease the implementation, this paper proposes a deep-frozen transfer learning framework, named FT-MDnet, to extract re-identification features from a pre-trained detection network in two steps. First, using a channel-wise attention mechanism, a network called adaptive transfer learning network (ATLnet) is used to convert the sharing data of the underlying detection network to a re-identification feature map. Then, a multi-branch feature representation network called multiple descriptor network (MDnet) is proposed to extract re-identification features from the re-identification feature map. Our proposed solution has been verified on different types of mainstream detection networks, including YOLOv3, YOLOv4, Mask RCNN, and CenterNet. The experimental results show that our solution outperforms all other person search solutions by a large margin. It proves that the feature representations of detection networks are highly compatible with re-identification, and the proposed framework effectively extracts these features out. To encourage further research, we have made our framework open source.

中文翻译：

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FT-MDnet：用于人员搜索的深度冻结迁移学习框架

在查询和候选之间匹配手动裁剪的行人图像，称为人员重新识别，在深度卷积神经网络方面取得了重大进展。最近，针对重新识别技术的端到端应用提出了一个名为“人员搜索”的主题。它集成了对象检测和人员重新识别，旨在在原始图像库上定位和匹配行人。然而，这种混合网络的设计和实现在实际情况中是困难的并且计算量很大。为了加快设计并简化实施，本文提出了一种名为 FT-MDnet 的深度冻结迁移学习框架，分两步从预训练的检测网络中提取重识别特征。首先，使用通道方式的注意力机制，一个称为自适应迁移学习网络（ATLnet）的网络用于将底层检测网络的共享数据转换为重识别特征图。然后，提出了一种称为多描述符网络（MDnet）的多分支特征表示网络，用于从重识别特征图中提取重识别特征。我们提出的解决方案已经在不同类型的主流检测网络上得到验证，包括 YOLOv3、YOLOv4、Mask RCNN 和 CenterNet。实验结果表明，我们的解决方案大大优于所有其他人员搜索解决方案。它证明了检测网络的特征表示与重识别高度兼容，并且所提出的框架有效地提取了这些特征。为鼓励进一步研究，