Tongji University

Hefei University of Technology

Beijing E-hualu Info Technology Co., Ltd.

Xinyong Zhao

Yang Zhao

Deshuang Huang

Jianhong Sun

Sijia Zheng

Yongjun Lin

Zhongqiu Zhao

The invention relates to a method for person re-identification based on deep model with multi-loss fusion training strategy. The method uses a deep learning technology to perform preprocessing operations such as flipping, clipping, random erasing and style transfer, and then feature extraction is performed through a backbone network model; joint training of a network is performed by fusing a plurality of loss functions. Compared with other deep learning-based person re-identification algorithms, the present invention greatly improves the performance of person re-identification by adopting a plurality of preprocessing modes, the fusion of three loss functions and effective training strategy.

Method for person re-identification based on deep model with multi-loss fusion training strategy

Growing community of inventors

Beijing, China

Xinyong Zhao