This paper proposes a novel real-time pose estimation system on embedded devices for a driver and a front passenger. The main goal of the proposed system is to operate in real time with limited hardware resources while preserving the high accuracy. The proposed system is divided into an object detection and a pose estimation. In the object detection, we eliminate the redundant and inaccurate bounding boxes by considering the characteristics of the target image domain. In the pose estimation, a single-person pose estimation with a lightweight deep learning model has been proposed and knowledge distillation has been adopted to maximize the performance while maintaining the high speed. In the experimental results, the proposed pose estimation has up to 9 % of the accuracy and the 9 times less computation compared to the previous methods. The operation speed is 195 frame per second on NVIDIA Jetson TX2.


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    Title :

    Lightweight Deep Neural Network-based Real-Time Pose Estimation on Embedded Systems


    Contributors:
    Heo, Junho (author) / Kim, Ginam (author) / Park, Jaeseo (author) / Kim, Yeonsu (author) / Cho, Sung-Sik (author) / Lee, Chang Won (author) / Kang, Suk-Ju (author)


    Publication date :

    2020-10-19


    Size :

    475293 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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