+7 (495) 957-77-43
Контакты

Article 9-8-2019

Извините, этот техт доступен только в “Американский Английский”. For the sake of viewer convenience, the content is shown below in the alternative language. You may click the link to switch the active language.

METHOD OF AUTOMATIC PEDESTRIAN RECOGNITION IN ROAD SCENE BY MICRO-DOPLER SIGNAL FOR SELF-DRIVING VEHICLE RADER SYSTEMS

Andrey V. Pluchevskiy, JSC «Cognitive», Moscow, Russia;
Tomsk State University of Control Systems and Radioelectronics (TUSUR), Tomsk, Russia,
pluch.andry@gmail.com

Abstract
This paper represents the method of automatic pedestrian recognition by a high-resolution Doppler spectrogram unique characteristic. The recognition is performed in a road scene with moving cars on the background. The Doppler spectrogram is regarded as a two-dimensional radar image. Taking into consideration the features of a pedestrian micro-Doppler signal, the processing of a two-dimensional radar image is reduced to a one-dimensional threshold. The algorithm has been developed for operating in a sliding window mode alongside the continuous acquisition of data on the target Doppler spectrum. The proposed technique has been developed analytically and does not require the use of machine learning and deep learning algorithms. Experimental research was conducted on two types of road scene objects: pedestrians and automobiles. The experiment results showed that the proposed method can distinguish a pedestrian from a moving automobile even if a Doppler bandwidth is similar. The work investigates the detection probability of the proposed method according to the signal-to-noise ratio and the false alarm probability. It enables to set requirements for a radar system on a design stage or to evaluate the possibility of applying the method in existing systems. The method is suitable for application in the radar and computer vision fields. The proposed technique was developed for use in driver assistance systems and the automotive vehicle industry to recognize pedestrians and take necessary measures for collision avoidance.

Keywords:micro-Doppler, Fourier transform, automatic target recognition, pedestrian recognition, cadence diagram, threshold, automotive radar.

References 

  1. Patole, S. M., Torlak, M., Wang, D., & Ali, M. (2017). Automotive radars: A review of signal processing techniques. IEEE Signal Processing Magazine, vol. 34, no. 2, pp. 22-35.
  2. Hasch, J. (2015). Driving towards 2020: Automotive radar technology trends. The 2015 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM), pp. 1-4.
  3. Kim, J., Han, D. S., & Senouci, B. (2018). Radar and vision sensor fusion for object detection in autonomous vehicle surroundings. The 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 76-78.
  4. Steinbaeck, J., Steger, C., Holweg, G., & Druml, N. (2017). Next generation radar sensors in automotive sensor fusion systems. The 2017 Sensor Data Fusion: Trends, Solutions, Applications (SDF), pp. 1-6.
  5. Gorelik A.L., Barabash Ju.L., Krivosheev O.V. (1990). Selekcija i raspoznavanie na osnove lokacionnoj informacii [Radar information based recognition and selection]. Moscow: Radio i svjaz, 240p. (in Russian)
  6. Pluchevskiy A.V. (2019). Micro-Doppler feature extraction of pedestrian and automobile using two-dimensional fast Fourier transform. T-Comm, vol. 13, no.5, pр. 61-68.
  7. Bartsch, A., Fitzek, F., & Rasshofer, R. H. (2012). Pedestrian recognition using automotive radar sensors. Advances in Radio Science, vol. 10, no. 2, pp. 45-55.
  8. Nanzer, J. A., & Rogers, R. L. (2009). Bayesian classification of humans and vehicles using micro-Doppler signals from a scanning-beam radar. IEEE Microwave and Wireless Components Letters, vol. 19, no. 5, pp. 338-340.
  9. Lee, S., Yoon, Y. J., Lee, J. E., & Kim, S. C. (2017). Human–vehicle classification using feature-based SVM in 77-GHz automotive FMCW radar. IET Radar, Sonar & Navigation, vol. 11, no. 10, pp. 1589-1596.
  10. Mesloub, A., Abed-Meraim, K., & Belouchrani, A. (2017). Ground moving target classification based on micro-Doppler signature using novel spectral information features. The 2017 European Radar Conference (EURAD), pp. 255-258.
  11. Zenaldin, M., & Narayanan, R. M. (2016). Radar micro-Doppler based human activity classification for indoor and outdoor environments, Proc. SPIE, [online] vol. 9829, no. 98291. Available at: http://dx.doi.org/10.1117/12.2228397 (Accessed 18 Feb.2019)
  12. Cao, P., Xia, W., & Li, Y. (2018). Classification of Ground Targets Based on Radar Micro-Doppler Signatures Using Deep Learning and Conventional Supervised Learning Methods. Radioengineering, vol. 27, no. 3, pp. 835-845.
  13. Erol, B., Amin, M. G., & Gurbuz, S. Z. (2018). Automatic data-driven frequency-warped cepstral feature design for micro-Doppler classification. IEEE Transactions on Aerospace and Electronic Systems, vol. 54, no. 4, pp. 1724-1738.
  14. Tivive, F. H. C., Phung, S. L., & Bouzerdoum, A. (2015).
    Classification of micro-Doppler signatures of human motions using
    log-Gabor filters. IET Radar, Sonar & Navigation, vol. 9, no. 9,
    pp. 1188-1195.
  15. Björklund, S., Petersson, H., & Hendeby, G. (2015). Features for micro-Doppler based activity classification. IET radar, sonar & navigation, vol. 9, no. 9, pp. 1181-1187.
  16. Kim, Y., & Moon, T. (2015). Human detection and activity classification based on micro-Doppler signatures using deep convolutional neural networks. IEEE geoscience and remote sensing letters, vol. 13, no. 1, pp. 8-12.
  17. Erol, B., & Gürbüz, S. Z. (2016). Hyperbolically-warped cepstral coefficients for improved micro-Doppler classification. The 2016 IEEE Radar Conference (RadarConf), pp. 1-6.
  18. Björklund, S., & Rydell, J. (2017). Micro-Doppler classification with boosting in perimeter protection. Belfast, UK. The International Conference on Radar Systems, pp. 23-26.
  19. Gürbüz, S. Z., Erol, B., Çağlıyan, B., & Tekeli, B. (2015). Operational assessment and adaptive selection of micro-Doppler features. IET Radar, Sonar & Navigation, vol. 9, no. 9, pp. 1196-1204.
  20. Du, R., Fan, Y., & Wang, J. (2018). Pedestrian and Bicyclist Identification Through Micro Doppler Signature With Different Approaching Aspect Angles. IEEE Sensors Journal, vol. 18, no. 9, pp. 3827-3835.
  21. Li, W., Xiong, B., & Kuang, G. (2017). Target classification and recognition based on micro-Doppler radar signatures. The 2017 Progress in Electromagnetics Research Symposium-Fall (PIERS-FALL), pp. 1679-1684.
  22. Fan, Y., Du, R., & Wang, J. (2018). Identification of Pedestrian and Bicyclist through Range Micro Doppler Signatures. IEICE Transactions on Information and Systems, vol. 101, no. 2, pp. 552-555.
  23. Naka, F. R., & Ward, W. W. (2000). Distant early warning line radars: The quest for automatic signal detection. Lincoln Laboratory Journal, vol. 12, no. 2, pp. 181-204.
  24. Perov A.I. (2003). Statisticheskaja teorija radiotehnicheskih system [Statistical theory of radio technical systems]. Moscow: Radiotehnika, 400p. (in Russian)
  25. Grishin, U.P., Ipatov, V.P. and Kazarynov, U.M. (1990). Radiotehnicheskie sistemy [Radar systems], in Kazarynov, U.M. (ed.). Moscow: Vysshaja shkola, 496p. (in Russian)
  26. Punt, M., Bruijn, S. M., Wittink, H., & van Dieën, J. H. (2015). Effect of arm swing strategy on local dynamic stability of human gait. Gait & posture, vol. 41, no. 2, pp. 504-509.
  27. Ghaleb, A., Vignaud, L., & Nicolas, J. M. (2008). Micro-Doppler analysis of pedestrians in ISAR imaging. The IET Signal Processing, vol. 2, no. 3, pp. 301-311.
  28. Garcia-Rubia, J. M., Kilic, O., Dang, V., Nguyen, Q. M., & Tran, N. (2014). Analysis of moving human micro-Doppler signature in forest environments. Progress In Electromagnetics Research, vol. 148, pp. 1-14.
  29. AWR1243 76-GHz to 81-GHz high-performance automotive MMIC evaluation module (2019). [online] Available at: http://www.ti.com/tool/AWR1243BOOST (Accessed 7 Jun.2019).

Information about author:
Pluchevskiy Andrey Vladimirovich, Junior development engineer, radiolocation department, JSC «Cognitive», Moscow, Russia;
graduate student, assistant, Tomsk State University of Control Systems and Radioelectronics (TUSUR), Tomsk, Russia