TY - GEN
T1 - Application of dimensionality-reduction algorithm in interaction action recognition of drivers
AU - Cheng, Qian
AU - Jiang, Xiao Bei
AU - Wang, Wu Hong
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2019.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Dimensionality-reduction algorithms
KW - Interaction action recognition
UR - https://www.scopus.com/pages/publications/85054480263
U2 - 10.1007/978-981-13-0302-9_85
DO - 10.1007/978-981-13-0302-9_85
M3 - Conference contribution
AN - SCOPUS:85054480263
SN - 9789811303012
T3 - Lecture Notes in Electrical Engineering
SP - 877
EP - 883
BT - Green Intelligent Transportation Systems - Proceedings of the 8th International Conference on Green Intelligent Transportation Systems and Safety
A2 - Wang, Wuhong
A2 - Jiang, Xiaobei
A2 - Bengler, Klaus
PB - Springer Verlag
T2 - 8th International Conference on Green Intelligent Transportation Systems and Safety, 2017
Y2 - 1 July 2017 through 2 July 2017
ER -