Tongji University

Hefei University of Technology

Beijing E-hualu Info Technology Co., Ltd.

Deshuang Huang

Yong Wu

Yang Zhao

Xinyong Zhao

Sijia Zheng

Jianhong Sun

Zhongqiu Zhao

Yongjun Lin

Disclosed is a cross-modality person re-identification method based on local information learning, the method comprising the following steps: acquiring a standard data set and performing data enhancement on the standard data set; dividing the enhanced standard data set into a training set and a test set; constructing a cross-modality person re-identification training network based on a dual-stream ResNet50 convolutional neural network architecture; inputting the training set into the cross-modality person re-identification training network to obtain a cross-modality person re-identification test network through training; randomly selecting an image to be queried from the test set, and inputting the image to be queried and a candidate database from the test set into the cross-modality person re-identification test network to obtain an identification accuracy value corresponding to the image to be queried.

Cross-modality person re-identification method based on local information learning

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

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Shanghai, China

Deshuang Huang