Abstract: Because of the rapid development of the convolutional neural network in recent years, the re-recognition of pedestrians has become another area of computer vision that is worthy of research after face recognition. The pedestrian re-recognition method involves verifying the similarity of two pedestrians or whether they are the same person and measuring the similarity of two features. And a pedestrian is a picture formed by multiple cameras, and its external conditions such as illumination, angle, and distance and it will increase the difficulty of verifying the similarities of two features. In this paper, a new method of pedestrian re-recognition based on combined Joint-Bayesian method and multi-camera shooting is proposed, which can effectively solve the problems caused by the change of shooting conditions of different cameras. Due to the good feature measurement ability of Joint-Bayesian, this paper studies a set of Joint-Bayesian matrices under different cameras, and combines with the global Joint Bayesian matrix to obtain a good recognition rate. On the Market-1501 and DukeMTMC-reID, the proposed method is used. The Rank-1 reached 88.09% and 80.07%, and the mAP reached 78.24% and 70.91%, which verified the effectiveness of the proposed method.

Key words: Pedestrian re-recognition, Joint-bayesian, Multi-camera condition

摘要： 由于最近几年卷积神经网络的快速发展，行人再识别也成为继人脸识别后又一个非常值得研究的计算机视觉领域。行人再识别涉及到验证两个人的相似性或是否为同一个人，度量两个特征的相似性。由多个相机拍摄条件下形成的行人图片，其外部条件如光线、拍摄角度、距离等因素会增加验证两个特征相似性的难度。提出了一种基于多相机拍摄结合联合贝叶斯矩阵的行人再识别方法，有效解决了不同相机拍摄条件变化带来的问题。由于联合贝叶斯良好的特征度量能力，在不同的相机拍摄条件下学习一组联合贝叶斯矩阵，并且与全局的联合贝叶斯矩阵进行结合，得到了很高的识别率。在Market-1501和DukeMTMC-reID数据集上，使用提出的方法进行了测试，其识别准确率达到了88.09%和80.07%，平均识别率达到了78.24%和70.91%，验证了所提出方法的有效性。

关键词: 行人再识别, 联合贝叶斯, 多相机