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Learning-Based People Counting System Using an IR-UWB Radar Sensor

IR-UWB 레이다 센서를 이용한 학습 기반 인원 계수 추정 시스템

  • Choi, Jae-Ho (Department of Electrical and Electronic Engineering, Pohang University of Science and Technology) ;
  • Kim, Ji-Eun (Department of Electrical and Electronic Engineering, Pohang University of Science and Technology) ;
  • Kim, Kyung-Tae (Department of Electrical and Electronic Engineering, Pohang University of Science and Technology)
  • 최재호 (포항공과대학교 전자전기공학과) ;
  • 김지은 (포항공과대학교 전자전기공학과) ;
  • 김경태 (포항공과대학교 전자전기공학과)
  • Received : 2018.11.14
  • Accepted : 2018.12.24
  • Published : 2019.01.31

Abstract

In this paper, we propose a real-time system for counting people. The proposed system uses an impulse radio ultra-wideband(IR-UWB) radar to estimate the number of people in a given location. The proposed system uses learning-based classification methods to count people more accurately. In other words, a feature vector database is constructed by exploiting the pattern of reflected signals, which depends on the number of people. Subsequently, a classifier is trained using this database. When a newly received signal data is acquired, the system automatically counts people using the pre-trained classifier. We validated the effectiveness of the proposed algorithm by presenting the results of real-time estimation of the number of people changing from 0 to 10 in an indoor environment.

Keywords

IR-UWB Radar;People Counting;Feature Extraction;Classification

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그림 1. 전 처리 수행 전의 IR-UWB 레이다 신호 Fig. 1. IR-UWB radar signal before applying signal prepro-cessing.

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그림 2. 전 처리 수행 후의 IR-UWB 레이다 신호 Fig. 2. IR-UWB radar signal after applying signal prepro-cessing.

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그림 3. 변형된 CLEAN 알고리즘 순서도 Fig. 3. The flowchart of modified CLEAN algorithm.

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그림 4. 변형된 CLEAN 알고리즘의 임계값 설정 Fig. 4. Threshold settings of modified CLEAN algorithm.

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그림 5. 다층 퍼셉트론 분류기 Fig. 5. The multi-layer perceptron classifier.

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그림 6. 실험 장소 1(트인 실내 공간) Fig. 6. Place of experiment 1(open indoor space).

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그림 7. 실험 장소 2(막힌 실내 공간) Fig. 7. Place of experiment 2(closed indoor space).

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그림 8. 실시간 인원 추정 Fig. 8. Real-time people counting.

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그림 9. 트인 실내 공간에서의 계수 추정 오차 행렬 Fig. 9. Confusion matrix of people counting in open indoor space.

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그림 10. 닫힌 실내 공간에서의 계수 추정 오차 행렬 Fig. 10. Confusion matrix of people counting in closed in-door space.

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그림 11. 트인 환경과 닫힌 환경에서의 계수 추정 결과 Fig. 11. The result of people counting in open and closed environment.

표 1. IR-UWB 레이다 사양 Table 1. The specification of an IR-UWB radar.

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Acknowledgement

Supported by : (주) 삼성전자

References

  1. A. Zanella, N. Bui, A. Castellani, L. Vangelista, and M. Zorzi, "Internet of things for smart cities," IEEE Internet of Things Journal, vol. 1, no. 1, pp. 22-32, Feb. 2014. https://doi.org/10.1109/JIOT.2014.2306328
  2. W. Balid, H. H. Refai, "On the development of self-powered IoT sensor for real-time traffic monitoring in smart cities," 2017 IEEE Sensors, Glasgow, Oct. 2017, pp. 1-3.
  3. T. Joseph, R. Jenu, A. K. Assis, V. A. S. Kumar, P. M. Sasi, and G. Alexander, "IoT middleware for smart city: An integrated and centrally managed IoT middleware for smart city," in 2017 IEEE Region 10 Symposium(TENSYMP), Cochin, Jul. 2017, pp. 1-5.
  4. C. Zeng, H. Ma, "Robust head-shoulder detection by PCAbased multilevel HOG-LBP detector for people counting," in 2010 20th International Conference on Pattern Recognition, Istanbul, Aug. 2010, pp. 2069-2072.
  5. Y. L. Hou, G. K. H. Pang, "People counting and human detection in a challenging situation," IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, vol. 41, no. 1, pp. 24-33, Jan. 2011. https://doi.org/10.1109/TSMCA.2010.2064299
  6. C. N. Padole, H. Proenca, "Periocular recognition: Analysis of performance degradation factors," in 2012 5th IAPR International Conference on Biometrics(ICB), New Delhi, Mar. 2012, pp. 439-445.
  7. J. W. Choi, X. Quan, and S. H. Cho, "Bi-directional passing people counting system based on IR-UWB radar sensors," IEEE Internet of Things Journal, vol. 5, no. 2, pp. 512-522, Apr. 2018. https://doi.org/10.1109/JIOT.2017.2714181
  8. F. Wahl, M. Milenkovic, and O. Amft, "A distributed pir-based approach for estimating people count in office environments," in 2012 IEEE 15th International Conference on Computational Science and Engineering, Nicosia, Dec. 2012, pp. 640-647.
  9. J. D. Taylor, Introduction to Ultra-Wideband Radar Systems, Boca Raton, CRC Press, 1994.
  10. J. W. Choi, S. S. Nam, and S. H. Cho, "Multi-human detection algorithm based on an impulse radio ultrawideband radar system," IEEE Access, vol. 4, pp. 10300-10309, Jan. 2017. https://doi.org/10.1109/ACCESS.2016.2647226
  11. J. W. Choi, J. H. Kim, and S. H. Cho, "A counting algorithm for multiple objects using an IR-UWB radar system," in 2012 3rd IEEE International Conference on Network Infrastructure and Digital Content, Beijing, Sep. 2012, pp. 591-595.
  12. J. W. Choi, S. H. Cho, "A crowdedness measurement algorithm using an IR-UWB radar sensor," in International Conference on Future Communication, Information and Computer Science(FCICS), Beijing, May 2014, pp. 119-122.
  13. X. Yang, W. Yin, and L. Zhang, "People counting based on CNN using IR-UWB radar," in 2017 IEEE/CIC International Conference on Communications in China (ICCC), Qingdao, Oct. 2017, pp. 1-5.
  14. J. W. Choi, D. H. Yim, and S. H. Cho, "People counting based on an IR-UWB radar sensor," IEEE Sensors Journal, vol. 17, no. 17, pp. 5717-5727, Sep. 2017. https://doi.org/10.1109/JSEN.2017.2723766
  15. K. T. Kim, D. K. Seo, and H. T. Kim "Efficient radar target recognition using the MUSIC algorithm and invariant features," IEEE Transactions on Antennas and Propagation, vol. 50, no. 3, pp. 325-337, Mar. 2002. https://doi.org/10.1109/8.999623
  16. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, New York, NY, John Wiley & Sons, 2012.
  17. C. M. Van der Walt, E. Barnard, "Data characteristics that determine classifier performance," SAIEE Africa Research Journal, vol. 98, no. 3, pp. 87-93, Nov. 2006.