| 基于多标签神经网络的行人属性识别 |
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| 引用本文：陈桂安?覮，王笑梅，刘鸿程.基于多标签神经网络的行人属性识别[J].计算技术与自动化,2020,(1):165-168 |
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| 中文摘要:在多标签行人属性识别的问题中，为了充分利用标签之间的相关性，解决传统方法识别准确率低和效率慢的问题，提出了一个多标签卷积神经网络。该网络在一个统一的网络框架下识别行人多个属性。把行人的多个属性看作是一个序列，然后构建了一个时序分类模型。提出的方法不仅避免了复杂的多输入MLCNN网络，也不需要多次训练单标签分类模型。实验结果表明，本文方法准确率均优于SIFT+SVM和多输入的MLCNN模型，平均准确率达到了90.41%。 |
| 中文关键词:多标签分类 神经网络 行人属性 深度学习 |
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| Pedestrian Attributes Recognition Based on Multi-label Neural Network |
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| Abstract:In the problem of multi-label pedestrian attributes recognition，in order to make full use of the correlation between labels and solve the problem of low recognition accuracy and low efficiency of traditional methods，a multi-label convolutional neural network is proposed，which is in a network. Identify multiple attributes of pedestrians under a unified network framework. We consider multiple attributes of a pedestrian as a sequence and then construct a time series classification model. The proposed method not only avoids the complicated multi-input MLCNN network，but also does not need to train the single-label classification model multiple times. The experimental results show that the accuracy of the proposed method is better than that of SIFT+SVM and multi-input MLCNN model，and the average accuracy rate is 90.41%. |
| keywords:multi-label classification neural network pedestrian attributes deep learning |
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