[1]王文龙,张 磊,张誉馨,等.行人属性识别:基于元学习的概率集成方法[J].计算机技术与发展,2022,32(03):71-75.[doi:10. 3969 / j. issn. 1673-629X. 2022. 03. 012]
　WANG Wen-long,ZHANG Lei,ZHANG Yu-xin,et al.Pedestrian Attribute Recognition:Probabilistic Ensemble LearningMethod Based on Meta-learning[J].,2022,32(03):71-75.[doi:10. 3969 / j. issn. 1673-629X. 2022. 03. 012]
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行人属性识别:基于元学习的概率集成方法()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年03期
页码:
71-75
栏目:
图形与图像
出版日期:
2022-03-10

文章信息/Info

Title:
Pedestrian Attribute Recognition:Probabilistic Ensemble LearningMethod Based on Meta-learning
文章编号:
1673-629X(2022)03-0071-05
作者:
王文龙1 ; 张 磊1 ; 张誉馨1 ; 吴晓富1 ; 张索非2
1. 南京邮电大学 通信与信息工程学院,江苏 南京 210003;
2. 南京邮电大学 物联网学院,江苏 南京 210003
Author(s):
WANG Wen-long1 ; ZHANG Lei1 ; ZHANG Yu-xin1 ; WU Xiao-fu1 ; ZHANG Suo-fei2
1. School of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;
2. School of Internet of Things,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
关键词:
卷积神经网络; 行人属性识别; 元学习; 概率集成; 行人检索
Keywords:
convolution neural network; pedestrian attribute recognition; meta-learning;  probabilistic ensemble; pedestrian retrieval
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 03. 012
摘要:
行人属性识别(pedestrian attribute recognition,PAR) 的目的是从输入图像中挖掘行人的属性信息。 近年来,卷积神经网络( convolution neural network,CNN)的兴起在行人属性识别中获得了广泛的应用。 现有的方法多采用属性不可知的视觉注意或启发式的身体部位定位机制来增强局部特征表达,而忽略了多模型集成所能够带来的提升,因此该领域内鲜少有集成算法的提出。 为了进一步提高行人属性识别的性能,该文从 CNN 模型的预测概率角度入手,基于元学习提出了一种行人属性识别的概率集成算法( probabilistic ensemble learning method,PEM) 。 在行人属性识别数据集 RAP 上的实验结果表明,该算法随着模型有效数的增加表现出递增的平均准确度( mean accuracy) 和 F1 值( F1-score),且评测结果均优于多个典型的行人属性识别算法。
Abstract:
Pedestrian attribute recognition ( PAR) aims to mine pedestrian attribute information from input images. In recent years, convolution neural network ( CNN) has been widely used in PAR. Existing methods mostly use visual attention with unknown attributes or heuristic body part localization mechanism to enhance local feature expression,but ignore the improvement brought by multi-model integration. Therefore,there are few integration algorithms proposed in this field.? ? ?In order to further improve the performance of PAR,we propose a probabilistic ensemble learning method based on meta-learning from the perspective of CNN model’s prediction probability.The experimental results on RAP dataset of pedestrian attribute recognition show that the proposed algorithm achieves improved meanaccuracy ( mA) and F1 score with the increase of models,which are better than several existing pedestrian attribute recognition algorithms.

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更新日期/Last Update: 2022-03-10