Person Re-identification Based on Novel Triplet Convolutional Neural Network
ZHU Jianqing① ZENG Huanqiang② DU Yongzhao① LEI Zhen③ ZHENG Lixin① CAI Canhui①
①(College of Engineering, Huaqiao University, Quanzhou 362021, China) ②(School of Information Science and Engineering, Huaqiao University, Xiamen 361021, China) ③(Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China)
Abstract：Most triplet Convolutional Neural Network (CNN) based person re-identification algorithms use the Euclidean distance as the similarity measurement between a pair of person images, and utilize the hinge loss function to train CNNs. However, there are two disadvantages in these approaches: the Euclidean distance is not discriminative enough for measuring person similarities; the margin parameter of the hinge loss function must be manually set in advance and it can not be adaptively adjusted. For these, a novel triplet convolutional neural network based person re-identification algorithm is proposed to solve the above two disadvantages for improving the accuracy. First, the normalization hybrid similarity function is proposed to replace Euclidean distance to obtain a more discriminative person similarity measurement. Second, the Log-logistic function is designed to replace the hinge function, which does not need to set the margin parameter so that the joint optimization effect of feature learning and similarity learning is improved. The experimental results on the Auto Detected CUHK03 and VIPeR databases show that the proposed method gains significant improvements in person re-identification accuracy, which verifies the superiority of the proposed method.
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