一种基于分块步态模板的鲁棒性步态识别方法

王科俊; 刘亮亮; 丁欣楠; 于凯强; 胡钢

doi:10.1631/FITEE.2000377

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一种基于分块步态模板的鲁棒性步态识别方法

常规文章 | Updated：2022-05-19

- 一种基于分块步态模板的鲁棒性步态识别方法
- A partition approach for robust gait recognition based on gait template fusion
- 信息与电子工程前沿（英文） 2021年22卷第5期页码：709-719
- Affiliations：
  
  College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
- Author bio：
  
  [ "Kejun WANG, E-mail: heukejun@126.com" ]
  Liangliang LIU, E-mail: liuliangliang@hrbeu.edu.cn
  [ "Xinnan DING, E-mail: dingxinnan@hrbeu.edu.cn" ]
  [ "Kaiqiang YU, E-mail: yukaiqiang@hrbeu.edu.cn" ]
  [ "Gang HU, E-mail: hugang@hrbeu.edu.cn" ]
- Funds：
  
  Project supported by the National Natural Science Foundation of China (No. 61573114)
- DOI：10.1631/FITEE.2000377
  中图分类号： TP391.4
- 收稿：2020-07-25，
  
  修回：2021-;2-;3，
  
  网络出版：2021-04-24，
  
  纸质出版：2021-05
- Accepted：
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王科俊, 刘亮亮, 丁欣楠, 等. 一种基于分块步态模板的鲁棒性步态识别方法[J]. 信息与电子工程前沿（英文）, 2021,22(5):709-719.

Kejun WANG, Liangliang LIU, Xinnan DING, et al. A partition approach for robust gait recognition based on gait template fusion[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(5): 709-719.
王科俊, 刘亮亮, 丁欣楠, 等. 一种基于分块步态模板的鲁棒性步态识别方法[J]. 信息与电子工程前沿（英文）, 2021,22(5):709-719. DOI： 10.1631/FITEE.2000377.

Kejun WANG, Liangliang LIU, Xinnan DING, et al. A partition approach for robust gait recognition based on gait template fusion[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(5): 709-719. DOI： 10.1631/FITEE.2000377.

摘要

步态识别具备远程识别的巨大潜力，但这种方法很容易受到与身份无关的因素影响，例如穿衣、随身携带的物体和角度。目前基于步态模板的方法可以有效表示步态特征。每一种步态模板都有其优势以及表征不同的显著信息。本文提出一种步态模板融合方法，以避免经典的步态模板（例如步态能量图像方法）的不足--经典步态模板表征的不完整信息对轮廓变化很敏感。所提步态模板融合方法采取分块的方法，以表征行人不同身体部位的不同步态习惯。根据人体各部分特点将融合的步态模板为3个部分（头部、躯干和腿部区域），然后将这3部分的步态模板分别输入卷积神经网络学习从而获得融合的步态特征。采用CASIA-B数据集进行充分的实验评估，并将所提方法与现有方法比较。实验结果表明，所提步态识别方法具有良好准确性和鲁棒性。

Abstract

Gait recognition has significant potential for remote human identification

but it is easily influenced by identity-unrelated factors such as clothing

carrying conditions

and view angles. Many gait templates have been presented that can effectively represent gait features. Each gait template has its advantages and can represent different prominent information. In this paper

gait template fusion is proposed to improve the classical representative gait template (such as a gait energy image) which represents incomplete information that is sensitive to changes in contour. We also present a partition method to reflect the different gait habits of different body parts of each pedestrian. The fused template is cropped into three parts (head

trunk

and leg regions) depending on the human body

and the three parts are then sent into the convolutional neural network to learn merged features. We present an extensive empirical evaluation of the CASIA-B dataset and compare the proposed method with existing ones. The results show good accuracy and robustness of the proposed method for gait recognition.

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references

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