Multi-loss joint optimization for person re-identification

Abstract

Due to the rise of deep learning, person re-identification has become a research hotspot in computer vision. For most person re-identification algorithm, softmax function is used as loss function which could increase the distance of interclasses, but has a bad convergence performance for the distance of intra-classes. Therefore, a person re-identification model based on multi-loss optimization is proposed by adding center loss. Center loss has the function of reducing intraclass distance, which makes up for the shortcoming of softmax loss. Two models are selected for comparative experiment to prove the effectiveness of our method. One is the re-ranking person re-identification model with kreciprocal coding, which is named IDE_ResNet-50+Jaccard. The other is the person re-identification model without kreciprocal coding, which is named IDE_ResNet-50. The experiments perform on the Market-1501 dataset, and the result shows that our method has a better result than the original model, which gains an increase of 1.25% and 0.63% in mAP and rank-1 accuracy for IDE_ResNet-50+Jaccard model. For the IDE_ResNet-50 model, the accuracy of mAP and rank1 increased by 1.86% and 0.18%, respectively.