关键词: 行人重识别, 多分支网络, 随机擦除, 局部特征, 深度学习

Abstract:

Most of the person re-identification algorithms combined with deep learning are mainly based on single branch network structure, and most of them use the global feature information of the picture, so it is easy to miss the key pedestrian information, which leads to the decline of metric learning effect and algorithm accuracy. Therefore, in order to make the network obtain more key pedestrian information and reduce the loss of obtaining pedestrian and detail information, the network learning of pedestrian characteristics is strengthened. Based on the network structure of resnet-50, the multi-branches network structure design is adopted. Considering the training difficulty and calculated amount, this paper chooses the structure which merging three independent branches that are consisted of random erasuring branch, global learning branch and local learning branch. On this basis, the adjustment and optimization are carried out according to the experimental data. Finally, the weight of loss function is allocated by combining the least square method to make the model more accurate. The experimental results show that the three branches structure are complementary. Compared with the basic single branch and multi-branch network, the algorithm improves the person re-identification algorithms accuracy.

Key words: pedestrian re-identification, multi-branch network, random erasing, local feature, deep learning