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Taking a Jump Motion Picture Automatically by using Accelerometer of Smart Phones

스마트폰 가속도계를 이용한 점프동작 자동인식 촬영

  • 최경윤 (인천대학교 임베디드시스템공학과) ;
  • 전경구 (인천대학교 임베디드시스템공학과)
  • Received : 2014.01.28
  • Accepted : 2014.07.21
  • Published : 2014.09.15

Abstract

This paper proposes algorithms to detect jump motion and automatically take a picture when the jump reaches its top. Based on the algorithms, we build jump-shot system by using accelerometer-equipped smart phones. Since the jump motion may vary depending on one's physical condition, gender, and age, it is critical to figure out common features which are independent from such differences. Also it is obvious that the detection algorithm needs to work in real-time because of the short duration of the jump. We propose two different algorithms considering these requirements and develop the system as a smart phone application. Through a series of experiments, we show that the system is able to successfully detect the jump motion and take a picture when it reaches the top.

본 논문에서는 자동으로 점프 동작을 인식하여 촬영하기 위한 점프 동작 인식 알고리즘을 제안하고, 가속도계가 장착된 스마트폰을 이용하여 촬영 시스템을 구현한다. 점프 동작은 개인의 신체적, 성별, 연령별 특성에 따라 다르기 때문에, 이들 특성에 비 의존적인 공통점을 감지할 수 있는 방법이 중요하다. 또한 점프 동작의 지속시간을 고려할 때 감지 알고리즘의 실시간 동작 가능성도 고려되어야 한다. 본 논문에서는 이를 고려한 두 가지 알고리즘을 제안하며, 이들을 스마트폰 어플리케이션으로 구현하여 촬영 실험을 진행하였다. 실험 결과 점프 동작 감지와 도약 동작을 포착한 사진 촬영이 가능하였다.

Keywords

References

  1. T.H. Kang, "Apparatus and method for capturing a jump image," Korea, Patent, 1020110060497 (2011. 06.08)
  2. http://www.youtube.com/watch?v=YZGcWj_mjn8
  3. T.B. Moeslund, A. Hilton, V. Kruger, "A survey of advances in vision-based human motion capture and analysis," Computer Vision and Image Understanding, Vol. 104, pp. 90-126, 2006. https://doi.org/10.1016/j.cviu.2006.08.002
  4. Q. Cai, J.K. Aggarwal, "Tracking Human Motion in Structured Environments Using a Distributed-Camera System," IEEE Transactions on Pattern Analysis and Machine intelligence, Vol. 21, No. 12, Nov. 1999.
  5. R. Cutler, L.S. Davis, "Robust Real-Time Periodic Motion Detection, Analysis, and Applications," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, Aug. 2000.
  6. H. Sidenbladh, "Detecting Human Motion with Support Vector Manchines," International Conference on Pattern Recognition, Vol. 2 pp. 188-191, Aug. 2004.
  7. R. Polana, R. Nelson, "Low Level Recognition of Human Motion," IEEE Workshop on Motion of Non-Rigid Articulated Objects, pp.77-82, Nov. 1994.
  8. K.T. Song, W.J. Chen, "Human Activity Recognition Using a Mobile Camera," International Conference on Ubiquitous Robots and Ambient Intelligence, pp. 3-8, Nov. 2011.
  9. J.T. Ryu, "The development of fall detection system using 3-axis acceleration sensor and tilt sensor," J Korea Industr Inf Syst Res, Vol. 18 No. 4, 2013. https://doi.org/10.9723/jksiis.2013.18.4.019
  10. S.H. Kim, J. Park, D.W. Kim, N.G. Kim, "The Study of Realtime Fall Detection System with Accelerometer and Tilt Sensor," Journal of the Korean Society for Precision Engineering, Vol. 28, No. 11, pp. 1330-1338, Nov. 2011.
  11. E. Thammasat, J. Chaicharn, "A Simply Fall-Detection Algorithm Using Accelerometers on a Smartphone," Biomedical Engineering International Conference (BMEiCON), pp. 1-4, Dec. 2012.
  12. H.J. Cho, S.C. Kim, "Motion Recognition with Smart Phone Embedded 3-Axis Accelerometer Sensor," IEEE International Conference on Systems, Man, and Cybernetics, pp. 919-924, Oct. 2012.
  13. Starlino Electronics, (2009 Dec. 29), A Guide to using Accelerometer and Gyroscope in Embedded Applications. Available: www.starlino.com/imu_guide.html (2013 Oct. 2)
  14. https://developer.bluetooth.org/