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

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


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.


Dimensionality-reduction algorithms Interaction action recognition 



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.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

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

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