Quantitative Evaluation for the Quality of the Military Parade

EasyChair Preprint 1805

Abstract

The highly synchronized parade can impress audiences how strong the troops seem to be, whereas it is difficult to train for the good parade because of its complex collective behavior. However, there is no scientific research about what the important factor to train and produce a good parade is. One of the bottlenecks to the scientific approach is the difficulty of measurement of a group as same as other swarm researches.　In this paper, we measured the posture data of members in the parade with OpenPose, which is a cutting-edge pose estimation technology of deep learning. By this measurement, we propose a numerical evaluation for the quality of the parade, and it is confirmed by questionnaire. In conclusion, our evaluation method is applicable for the quantitative evaluation, and it was suggested that the variation level of the arm swing angles was related to the quality of the parade.

Keyphrases: Autonomous distributed system, OpenPose, Parade, collective behavior

@booklet{EasyChair:1805,
  author    = {Yohei Okugawa and Masao Kubo and Hiroshi Sato},
  title     = {Quantitative Evaluation for the Quality of the Military Parade},
  howpublished = {EasyChair Preprint 1805},
  year      = {EasyChair, 2019}}