Human Body Target Recognition Under Occlusion Based on Fusion of Image Contour Moment and Harris Angular Points

YANG Yun; YUE Zhu

doi:10.3788/YJYXS20132802.0273

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Human Body Target Recognition Under Occlusion Based on Fusion of Image Contour Moment and Harris Angular Points

Imaging Technology & Image Processing | 更新时间：2020-08-12

- Human Body Target Recognition Under Occlusion Based on Fusion of Image Contour Moment and Harris Angular Points
- Chinese Journal of Liquid Crystals and Displays Vol. 28, Issue 2, Pages: 273-277(2013)
- 作者机构：
  
  陕西科技大学电气与信息工程学院,陕西西安,710021
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20132802.0273
  CLC： TP391.41
- Received：14 August 2012，
  
  Revised：08 September 2012，
  
  Published：15 April 2013
- 稿件说明：
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杨云, 岳柱. 基于融合图像轮廓矩和Harris角点方法的遮挡人体目标识别研究[J]. 液晶与显示, 2013,28(2): 273-277

YANG Yun, YUE Zhu. Human Body Target Recognition Under Occlusion Based on Fusion of Image Contour Moment and Harris Angular Points[J]. , 2013,28(2): 273-277
杨云, 岳柱. 基于融合图像轮廓矩和Harris角点方法的遮挡人体目标识别研究[J]. 液晶与显示, 2013,28(2): 273-277 DOI： 10.3788/YJYXS20132802.0273.

YANG Yun, YUE Zhu. Human Body Target Recognition Under Occlusion Based on Fusion of Image Contour Moment and Harris Angular Points[J]. , 2013,28(2): 273-277 DOI： 10.3788/YJYXS20132802.0273.

摘要

针对遮挡人体目标识别中人体特征描述容易受到遮挡物影响

但人体的头肩部分不易受到遮挡的特点

提出了一种基于人体头肩部位混合轮廓特征的遮挡人体目标识别的新方法。首先利用背景差分法把人体从复杂的背景中分离出来

接着利用人体的先验知识提取出头肩部位

然后提取出头肩部位轮廓的矩不变量和Harris角点特征

然后将2种特征融合构成混合特征

最后将新特征输入BP神经网络进行识别。实验表明:此方法具有较好的鲁棒性和较高的识别率。

Abstract

For the human object recognition under occlusion

the human characteristics description are vulnerably effected under occlusion

but the head and shoulder of the human body are less susceptible to the characteristics of the block

put forward a new method based on the human head and shoulder mixed contour features for human object recognition under occlusion. First

using background subtraction method to separate the human body from the complex background

and then using a priori knowledge of the human body to extract the head and shoulder parts

then the contour moment invariant and Harris angular points are extracted and fused into a mixed feature

finally put the mixed feature into BP neural network to recognize. The experiments show that the method has better robustness and higher recognition rate.

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references

Zhou J P, Hoang J. Real time robust human detection and tracking system [C].Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego,USA:IEEE,2005:149-156.[2] 毛以芳, 沈小莉, 李英哲,等. 基于头肩矩特征的人体识别研究 [J]. 科技信息, 2009,(16):443-444.[3] 宫宇. 运动人体检测和二维关键点提取 [J]. 电子设计工程,2012,(20):168-170.[4] 王飞杰.基于头肩特征的人体实时监测与跟踪 [D].长春:吉林大学硕士论文,2010.[5] 雷海龙, 杨胜, 刘红新. 融合图像轮廓矩和傅立叶描叙子的手势识别 [J]. 微电子学与计算机,2011,4(28):103-106.[6] Schmid C, Mohr R, Bauckhage C. Evaluation of interest point detectors [J]. International Journal of Computer Vision, 2000,37(2):151-172.[7] 沈凌云,郎百和,朱明.一种基于人工神经网络的人脸识别方法 [J].液晶与显示,2011,26(6):836-840.[8] Yu S W, Zhu K J, Fengqin Diao. A dynamic all parameters adaptive BP neural networks model and its application on oil reservoir prediction [J]. Applied Mathematics and Computation,2008,195(1):66-75.

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