Pedestrian detection method based on Faster RCNN

ZHANG Hui; DU Yu; NING Shu-rong; ZHANG Yong-hua; YANG Shuo; DU Chen

doi:10.13873/j.1000-9787(2019)02-0147-03

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Pedestrian detection method based on Faster RCNN

更新时间：2025-09-11

- Pedestrian detection method based on Faster RCNN
- Issue 2, Pages: 147-149+153(2019)
- 作者机构：
  
  1. 北京联合大学智慧城市学院
  2. 北京联合大学机器人学院
  3. 北京市信息服务工程重点实验室
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-9787(2019)02-0147-03
  CLC： TP183;TP391.41
- Published：2019
- 稿件说明：
移动端阅览
ZHANG Hui, DU Yu, NING Shu-rong, et al. Pedestrian detection method based on Faster RCNN[J]. 2019, (2): 147-149+153.
DOI：

ZHANG Hui, DU Yu, NING Shu-rong, et al. Pedestrian detection method based on Faster RCNN[J]. 2019, (2): 147-149+153. DOI： 10.13873/j.1000-9787(2019)02-0147-03.

摘要

借鉴目标识别领域的快速区域卷积神经网络(Fast RCNN)

提出了基于Faster RCNN的行人检测方法

利用CNN提取图像特征

通过聚类和构建区域建议网络(RPN)提取可能含有行人的区域

利用检测网络对目标区域进行判别和分类

并在INRIA数据集中进行了测试验证。实验结果表明:相比基于可变形的组件模型(DPM)的行人检测方法

提出的方法

在测试集上检测准确度达到92. 7%

相比现有的其他方法

其检测效果更好。

Abstract

Using Fast region-based convolutional neural network（ Fast RCNN） for reference

propose pedestrian detection method based on Faster RCNN. in target recognition field

. Image features are extracted by CNN. A region proposal network（ RPN） is built up to extract regions that might contain pedestrians combined with K-means cluster analysis. And the region is identified and classified by detection network. The method is tested in the INRIA dataset. Experimental results show that compared with the method of pedestrian detection based on DMP

the proposed method achieves the accuracy of 92. 7 %

it performs better

compared with other algorithms.

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