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Application of Dimensionality-Reduction Algorithm in Interaction Action Recognition of Drivers

  • Cheng Qian 
  • Jiang Xiao-bei 
  • Wang Wu-hong 
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 503)

Abstract

Human action recognition has many applications including design of human–machine system. Identifying the interaction between the driver and the vehicle information system is necessary to accurately identify the driver’s intention and improve the stability of the vehicle. A machine learning-based framework for interaction action recognition of drivers was proposed in this chapter. Several dimensionality-reduction algorithms (PCA, Isomap, LLE, LE) for interaction action recognition are compared in this chapter. The test sequence is mapped into a low-dimensional space through these dimensionality-reduction algorithms, and traditional classifiers (naïve Gaussian, logistic regression, SVM, Kneighbors, DecisionTree) were trained in order to test the effect of dimensionality-reduction. Results show that “LLE+SVM” achieves the highest precision rate.

Keywords

Dimensionality-reduction algorithms Interaction action recognition 

Notes

Acknowledgements

This research is partially supported by the Beijing Institute of Technology International Science and Technology cooperation Project (GZ2016035102), and the Project Based Personnel Exchange Program with China Scholarship Council and German Academic Exchange Service.

References

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    Wang KJ, Liu LL, Ben XY et al (2009) Gait recognition based on energy image and two dimensional principal component analysis. J Image Graph 14(12):2503–2509Google Scholar
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    Saul LK, Roweis ST (2003) Think globally, fit locally: unsupervised learning of low dimensional manifolds. J Mach Learn Res 4:119–155MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Cheng Qian 
    • 1
  • Jiang Xiao-bei 
    • 1
  • Wang Wu-hong 
    • 1
  1. 1.Department of Transportation EngineeringBeijing Institute of TechnologyBeijingPeople’s Republic of China

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