计算机科学 ›› 2017, Vol. 44 ›› Issue (Z11): 260-262.doi: 10.11896/j.issn.1002-137X.2017.11A.054

• 模式识别与图像处理 • 上一篇    下一篇

非接触式心率检测方法的颜色空间选择

曹剑剑,冯军,汤文明,余瑛   

  1. 江西中医药大学 南昌330004,江西中医药大学 南昌330004,江西中医药大学 南昌330004,江西中医药大学 南昌330004
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受江西中医药大学校级课题(2014jzzdxk021,2014jzyb-3,2016jzgy-06)资助

Color Space Selection of Non-contact Heart Rate Measurement

CAO Jian-jian, FENG Jun, TANG Wen-ming and YU Ying   

  • Online:2018-12-01 Published:2018-12-01

摘要: 采用非接触式方法进行人体心率检测,较传统接触式的测量方法更加便捷舒适。基于图像光学容积(iPPG)的非接触式心率测量方法,简要分析该方法的实现流程及颜色空间产生测量误差的机理,设计实验来对比不同颜色空间的测量误差,一方面证明了在非接触式心率测量过程中进行颜色空间选取的必要性,另一方面获得了可用以提高心率测量精度的最优颜色空间。实验结果表明,与同一时刻采用心电测量仪(ECG)得到的心率相比,采用RGB颜色空间进行心率测量的误差最小,实验的平均误差达到1.68 bpm。因此,在非接触式心率测量中选择RGB颜色空间可以达到更高的精度。

关键词: 颜色空间,非接触,心率,图像容积

Abstract: Compared with the traditional contact measurement method,the non-contact method for heart rate detection is more convenient and comfortable.Based on the non-contact heart rate measurement method of image-photoplethysmography(iPPG),this paper briefly analyzed the mechanism of this method and the measurement error of color space.The experiment compares the measurement error according to the different color space,which proves the necessity of selecting the color space during the non-contact heart rate measurement. On the other hand,optimal color space can be used to improve the accuracy of heart rate measurement accuracy of the color space.The experimental results show that the heart rate measurement with RGB color space has the smallest error compared with the heart rate obtained by electrocardiograph(ECG) at the same time.The average error of experiment is 1.68 bpm.Thus,choosing RGB color in non-contact heart rate measurement space can get higher accuracy.

Key words: Color space,Non-contact,Heart rate,Image-photo plethysmography

[1] IKEUCHI K.Computer Vision:A Reference Guide[M].Sprin-ger Publishing Company,Incorporated,2014.
[2] 苏松志,李绍滋,陈淑媛,等.行人检测技术综述[J].电子学报,2012,40(4):814-820.
[3] CAO J,PANG Y,LI X.Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry[J].IEEE Transactions on Image Processing a Publication of the IEEE Signal Processing Society,2016,5(12):5538.
[4] KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNetClassification with Deep Convolutional Neural Networks[J].Advances in Neural Information Processing Systems,2012,25(2):2012.
[5] GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation[C]∥CVPR’14.2014:580-587.
[6] REDMON J,DIiVVALA S,GIRSHICK R,et al.You Only Look Once:Unified,Real-Time Object Detection[C]∥CVPR’16.2016:779-788.
[7] DALAL N,TRIGGS B.Histograms of oriented gradients forhuman detection[C]∥IEEE Conference on Computer Vision & Pattern Recognition.2005:886-893.
[8] ZHANG S,BAUCKHAGE C,CREMERS A B.Informed Haar-Like Features Improve Pedestrian Detection[C]∥IEEE Confe-rence on Computer Vision and Pattern Recognition.2014:947-954.
[9] WANG X G.Deep learning in image recognition[J].Communications of the CCF,2015,11(8):15-23.
[10] DOLLR P,TU Z,PERONA P,et al.Integral Channel Features[C]∥British Machine Vision Conference,BMVC 2009.London,UK,2009.
[11] ZHANG S,BENESON R,OMRAN M,et al.How Far are We from Solving Pedestrian Detection?[C]∥ CVPR’16.2016:1259-1267.
[12] DOLLAR P,APPEL R,BELONGIE S,et al.Fast Feature Pyramids for Object Detection[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2014,36(8):1532-1545.
[13] FARINELLA G M,RAV D,et al.Representing scenes for real-time context classification on mobile devices[J].Pattern Recognition,2015,48(4):1086-1100.
[14] WALLACE G K.The JPEG still picture compression standard[J].Communications of the ACM,1991,34(4):30-44.
[15] STURGESS P,ALAHARI K,LADICKY L,et al.CombiningAppearance and Structure from Motion Features for Road Scene Understanding[C]∥British Machine Vision Conference,BMVC 2009.London,UK,2009.
[16] BATTIATO S,MANCUSO M,BOSCO A,et al.Psychovisualand Statistical Optimization of Quantization Tables for DCT Compression Engines[C]∥International Conference on Image Analysis and Processing.IEEE Computer Society,2001:602-606.
[17] PETERSON H A,PENG H,MORGAN J H,et al.Quantization of color image components in the DCT domain[C]∥Proceedings of SPIE-The International Society for Optical Engineering.1991:210-222.
[18] LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-based lear-ning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2232.

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