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Neural Computing and Applications

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06-09-2019 | Advances in Parallel and Distributed Computing for Neural Computing

Hierarchical attributes learning for pedestrian re-identification via parallel stochastic gradient descent combined with momentum correction and adaptive learning rate

Authors: Keyang Cheng, Fei Tao, Yongzhao Zhan, Maozhen Li, Kenli Li

Published in: Neural Computing and Applications | Issue 10/2020

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Abstract

Convolutional neural networks (CNNs) have obtained high accuracy results for pedestrian re-identification in the past few years. There is always a trade-off between high accuracy and computational time in CNNs. Training CNN is always very difficult as it may take a long time to produce high accuracy results. To overcome this limitation, a novel method parallel stochastic gradient descent (PSGD) is proposed to train a five-hierarchical parallel CNNs that is designed according to pedestrian attributes. Moreover, the momentum correction and adaptive adjustment of learning rate are applied during training process and the time interval for updating parameters is inspected during optimization of parameters selection. The results of this paper prove the effectiveness of proposed PSGD that successfully decreases the training process by five times and surpasses the state-of-the-art methods of pedestrian re-identification in terms of both accuracy and time. The minimum reported running time of the proposed method is 8.7 s which is minimum among all other state-of-the-art methods. These promising results show the efficiency and performance of the proposed model.

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Title: Hierarchical attributes learning for pedestrian re-identification via parallel stochastic gradient descent combined with momentum correction and adaptive learning rate
Authors: Keyang Cheng
Fei Tao
Yongzhao Zhan
Maozhen Li
Kenli Li
Publication date: 06-09-2019
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 10/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-019-04485-2

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