Analyzing and Recognizing Pedestrian Motion Using 3D Sensor Network and Machine Learning

  • Ningping SunEmail author
  • Toru Tsuruoka
  • Shunsuke Murakami
  • Takuma Sakamoto
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 927)


How to analyze and recognize pedestrian movements is an important issue dependent on motion capture devices. In our work, we used two types of popular 3D sensors such as 3D depth sensor and 3D motion sensor to construct a sensor network for tacking motion of target because of their convenience and low cost. In this paper, we first describe how to get data from the sensor network and how to process raw data. Next, we provide algorithms for applying machine learning to the analysis and recognition of human motions. Finally, we give some evaluation experimental results.


  1. 1.
    Sun, N., Murakami, S.: Human motion modeling from complementary skeleton joints of multiple kinects. In: Proceedings of the 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms. Advances in Intelligent Systems Research, vol. 159, pp. 131–135 (2018)Google Scholar
  2. 2.
    Sun, N., Sakai, Y.: New approaches to human gait simulation using motion sensors. In: 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA) (2017). 16901760Google Scholar
  3. 3.
    Sun, N., Tsuruoka, T.: Pedestrian action recognition using motion sensor and k-nn classifier. In: Proceedings of 2nd International Conference on Artificial Intelligence: Technologies and Applications. Advances in Intelligent Systems Research, vol. 146, pp. 1–4 (2018)Google Scholar
  4. 4.
    Miyajima, S., Tanaka, T., Miyata, N., Tada, M., Mochimaru, M., Izumi, H.: Feature selection for work recognition and working motion measurement. J. Robot. Mechatron. 30(5), 706–716 (2018)CrossRefGoogle Scholar
  5. 5.
    Foley, J.D., Cardelli, L., van Dam, A., Feiner, S.K., Hughess, J.F.: Computer Graphics: Principles and Practice, 3rd edn., pp. 263–286, 299–320. Addison-Wesley, New York (2015)Google Scholar
  6. 6.
    Lambrecht, S., Nogueira, S.L., Bortole, M., Siqueira, A.A.G., Terra, M.H., Rocon, E., Pons, J.L.: Inertial sensor error reduction through calibration and sensor fusion. Sensors 16(2), 1–16 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ningping Sun
    • 1
    Email author
  • Toru Tsuruoka
    • 2
  • Shunsuke Murakami
    • 2
  • Takuma Sakamoto
    • 2
  1. 1.Department of Human-Oriented Information System EngineeringNational Institute of Technology, Kumamoto CollegeKoshiJapan
  2. 2.Advanced Electronics and Information Systems Engineering CourseNational Institute of Technology, Kumamoto CollegeKoshiJapan

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