高速かつ省メモリなドライバ姿勢推定

DOI Web Site 21 References
  • Nishiyuki Kenta
    Vision Sensing Lab., Technology Research Center, Technology and Intellectual Property H.Q., OMRON Corporation
  • Hyuga Tadashi
    Vision Sensing Lab., Technology Research Center, Technology and Intellectual Property H.Q., OMRON Corporation
  • Tasaki Hiroshi
    Vision Sensing Lab., Technology Research Center, Technology and Intellectual Property H.Q., OMRON Corporation
  • Kinoshita Koichi
    Vision Sensing Lab., Technology Research Center, Technology and Intellectual Property H.Q., OMRON Corporation
  • Hasegawa Yuki
    Vision Sensing Lab., Technology Research Center, Technology and Intellectual Property H.Q., OMRON Corporation
  • Yamashita Takayoshi
    Department of Information Engineering, Chubu University
  • Fujiyoshi Hironobu
    Department of Robotic Science and Technology, Chubu University

Bibliographic Information

Other Title
  • Fast and Compact Driver Pose Estimation

Abstract

<p>Driver pose estimation is a key component in driver monitoring systems, which is helpful for driver anomaly detection. Compared with traditional human pose estimation, driver pose estimation is required to be fast and compact for embedded systems. We propose fast and compact driver pose estimation that is composed of ShuffleNet V2 and integral regression. ShuffleNet V2 can reduce computational expense, and integral regression reduce quantization error of heat maps. If a driver suddenly gets seriously ill, the head of the driver is out of view. Therefore, in addition to localizing body parts, classifying whether each body part is out of view is also crucial for driver anomaly detection. We also propose a novel model which can localize and detect each body part of the driver at once. Extensive experiments have been conducted on a driver pose estimation dataset recorded with near infrared camera which can capture a driver at night. Our method achieves large improvement compared to the state-of-the-art human pose estimation methods with limited computation resources. Futhermore, We perform an ablation study of our method which composed of ShuffleNet V2, integral regression, and driver body parts detection. Finally, we show experimental results of each driver action for driver monitoring systems.</p>

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