Pedestrian detection and re-identification technology is a research hotspot in the field of computer vision. This technology currently has issues such as insufficient pedestrian expression ability, occlusion, diverse pedestrian attitude, and difficulty of small-scale pedestrian detection. In this paper, we proposed an end-to-end pedestrian detection and re-identification model in real scenes, which can effectively solve these problems. In our model, the original images are processed with a non-overlapped image blocking data augmentation method, and then input them into the YOLOv3 detector to obtain the object position information. LCNN-based pedestrian re-identification model is used to extract the features of the object. Furthermore, the eigenvectors of the object and the detected pedestrians are calculated, and the similarity between them are used to determine whether they can be marked as target pedestrians. Our method is lightweight and end-to-end, which can be applied to the real scenes.

行人检测和重新识别技术是计算机视觉领域的研究热点。目前，该技术存在诸如行人表达能力不足，遮挡，行人姿态多样以及小规模行人检测困难等问题。本文提出了一种真实场景下的端到端行人检测与重识别模型，可以有效解决这些问题。在我们的模型中，原始图像用不重叠的图像块数据增强方法进行处理，然后将它们输入到YOLOv3检测器中以获得物体位置信息。基于LCNN的行人重识别模型用于提取对象的特征。此外，计算物体和检测到的行人的特征向量，它们之间的相似性用于确定是否可以将它们标记为目标行人。我们的方法是轻量级的并且是端到端的，可以应用于真实场景。