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


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.


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

JJPHCH_2019_v30n1_28_f0001.png 이미지

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

JJPHCH_2019_v30n1_28_f0002.png 이미지

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

JJPHCH_2019_v30n1_28_f0003.png 이미지

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

JJPHCH_2019_v30n1_28_f0004.png 이미지

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

JJPHCH_2019_v30n1_28_f0005.png 이미지

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

JJPHCH_2019_v30n1_28_f0006.png 이미지

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

JJPHCH_2019_v30n1_28_f0007.png 이미지

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

JJPHCH_2019_v30n1_28_f0008.png 이미지

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

JJPHCH_2019_v30n1_28_f0009.png 이미지

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

JJPHCH_2019_v30n1_28_f0010.png 이미지

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

JJPHCH_2019_v30n1_28_f0011.png 이미지

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.

JJPHCH_2019_v30n1_28_t0001.png 이미지


Supported by : (주) 삼성전자


  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.
  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.
  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.
  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.
  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.
  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.
  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.