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CHENG Wen-dong, MA Yong, WEI Qing-yuan. Hand gesture recognition method in driver's phone-call behavior based on decision fusion of image features[J]. Journal of Traffic and Transportation Engineering, 2019, 19(4): 171-181. doi: 10.19818/j.cnki.1671-1637.2019.04.016
Citation: CHENG Wen-dong, MA Yong, WEI Qing-yuan. Hand gesture recognition method in driver's phone-call behavior based on decision fusion of image features[J]. Journal of Traffic and Transportation Engineering, 2019, 19(4): 171-181. doi: 10.19818/j.cnki.1671-1637.2019.04.016
shu

Hand gesture recognition method in driver's phone-call behavior based on decision fusion of image features

doi: 10.19818/j.cnki.1671-1637.2019.04.016
  • 1.

    School of Mechatronic Engineering, Xi'an Technological University, Xi'an 710021, Shaanxi, China

  • 2.

    School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China

  • 3.

    School of Mechanical Engineering, Harbin Institute of Petroleum, Harbin 150028, Heilongjiang, China

More Information
  • Author Bio:

    CHENG Wen-dong(1981-), male, associate professor, PhD, chengwendong@foxmail.com

  • Received Date: 2019-02-15
  • Publish Date: 2019-08-25
    Abstract
  • In order to detect drivers' phone-call behavior robustly in natural environment, a hand gesture recognition method was proposed. The Adaboost algorithm was used to detect driver's face region. In YCgCr color space, the brightness component and chroma component of facial skin were sampled by sparse grid, respectively, and a Gaussian distribution model of skin color was built. Considering the inhomogeneity of cab illumination, a skin color component drift compensation algorithm was proposed, and an online skin color model was established to adapt the changes of illumination, so that the skin color regions of right and left hands can be accurately segmented. The 2 376 dimensions HOG feature vector of hand skin region was extracted by HOG algorithm, and then PCA method was used to reduce HOG feature vector to 400 dimensions. Meanwhile, the PZMs features of hand skin region were extracted and 8 PZMs feature vectors with the largest weights were screened out by Relief algorithm. A support vector machine classifier decision for phone-call hand gesture was established based on the PCA-HOG and Relief-PZMs features. Experimental result shows that the hand gesture recognition rate based on the PCA-HOG features is 93.1%, and it has good robust to illumination changes but is easily disturbed by hand and head rotation. The hand gesture recognition rate based on the Relief-PZMs features is 91.9%, and it has good tolerance to head and hand gestures but has poor illumination robustness. The hand gesture recognition rate of the proposed multi-feature-fusion method combined with the PCA-HOG and Relief-PZMs is up to 94.5%, and it has good adaptability to illumination fluctuate, hand and head rotation, and other interference conditions.

     

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