Person Re-identification Based on Multi-information Flow Convolutional Neural Network
SANG Hai-feng1, WANG Chuan-zheng1, LÜ Ying-yu1, HE Da-kuo2, LIU Qing2
1. School of Information Science & Engineering, Shenyang University of Technology, Shenyang, Liaoning 110870, China;
2. College of Information Science & Engineering, Northeastern University, Shenyang, Liaoning 110819, China
Abstract：It is challenging to learn efficient features in person re-identification task due to complex variations of viewpoints,illumination,pose etc.In addition,deep neural network still suffers from overfitting with a small training set.To solve these problems,a Multi-information Flow Convolutional Neural Network(MiF-CNN)is designed for person re-identification which contains a specific convolutional architecture.In this architecture,features from each convolution layer are concatenated with all subsequent convolution layers so that it can improve the flowability of feature information and the efficiency of backpropagation gradient.In this way,the proposed network can learn more discriminative features.Moreover,a combination of three loss functions is used to train proposed network.Finally,the identification accuracy is obtained by ranking the similarity of extracted features measured by Euclidean distance.Experimental results on the VIPeR and CUHK01 datasets demonstrate that MiF-CNN outperform most of existing methods of person re-identification and reduce overfitting more effectively.
桑海峰, 王传正, 吕应宇, 何大阔, 刘晴. 基于多信息流动卷积神经网络的行人再识别[J]. 电子学报, 2019, 47(2): 351-357.
SANG Hai-feng, WANG Chuan-zheng, LÜ Ying-yu, HE Da-kuo, LIU Qing. Person Re-identification Based on Multi-information Flow Convolutional Neural Network. Acta Electronica Sinica, 2019, 47(2): 351-357.
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2019, Vol. 47 
