| 基于多标签神经网络的行人属性识别 |
投稿时间：2019-02-11 修订日期：2019-03-05  点此下载全文 |
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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,我们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。我们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。本文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, we propose a multi-label convolutional neural network, 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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