基于多标签神经网络的行人属性识别  

Pedestrian Attributes Recognition Based on Multi-label Neural Network

作　　者：陈桂安 王笑梅[1] 刘鸿程 CHEN Gui-an;WANG Xiao-mei;LIU Hong-cheng(Shanghai Normal University,Shanghai 200234,China)

机构地区：[1]上海师范大学,上海200234

出　　处：《计算技术与自动化》2020年第1期165-168,共4页Computing Technology and Automation

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%。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%.

关 键 词：多标签分类 神经网络 行人属性 深度学习 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]