Estimation of Pedestrian Height Using Uncalibrated Cameras

  • Alejandro Valdés-Camejo
  • Guillermo Aguirre-Carrazana
  • Raúl Alonso-Baryolo
  • Annette Morales-González
  • Francisco J. Silva-Mata
  • Edel García-Reyes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)

Abstract

The height of a person can be used as a Soft Biometrics feature in surveillance scenarios. The automatic estimation of pedestrian height have been addressed mostly on calibrated cameras. We are proposing a new method for real height estimation in videos from uncalibrated cameras. Our proposal computes the horizon line within the scene and then, the relative height of each person is obtained. We employ the real height distribution of a population to provide the final height value. In this process, it has been included an evaluation of the silhouette’s quality, in order to improve the results. Experiments were conducted in uncontrolled scenarios, showing a good performance of our method.

Keywords

Height estimation Video surveillance Silhouette quality Shape matching Uncalibrated camera 

References

  1. 1.
    Dantcheva, A., Velardo, C., Dangelo, A., Dugelay, J.L.: Bag of soft biometrics for person identification. Multimedia Tools Appl. 51(2), 739–777 (2011)CrossRefGoogle Scholar
  2. 2.
    Hansen, D.M., Mortensen, B.K., Duizer, P.T., Andersen, J.R., Moeslund, T.B.: Automatic annotation of humans in surveillance video. In: Fourth Canadian Conference on Computer and Robot Vision, CRV 2007, pp. 473–480. IEEE (2007)Google Scholar
  3. 3.
    Kispál, I., Jeges, E.: Human height estimation using a calibrated camera. In: Proceedings of the CVPR (2008)Google Scholar
  4. 4.
    Arigbabu, O.A., Ahmad, S.M.S., Adnan, W.A.W., Yussof, S., Iranmanesh, V., Malallah, F.L.: Estimating body related soft biometric traits in video frames. Sci. World J. 2014 (2014)Google Scholar
  5. 5.
    Jung, J., Kim, H., Yoon, I., Paik, J.: Human height analysis using multiple uncalibrated cameras. In: 2016 IEEE International Conference on Consumer Electronics (ICCE), pp. 213–214. IEEE (2016)Google Scholar
  6. 6.
    Jung, J., Yoon, I., Lee, S., Paik, J.: Object detection and tracking-based camera calibration for normalized human height estimation. J. Sens. 2016, 8347841:1–8347841:9 (2016)CrossRefGoogle Scholar
  7. 7.
    Criminisi, A., Zisserman, A., Van Gool, L., Bramble, S., Compton, D.: A new approach to obtain height measurements from video. In: Proceedings of SPIE, vol. 3576 (1998)Google Scholar
  8. 8.
    De Angelis, D., Sala, R., Cantatore, A., Poppa, P., Dufour, M., Grandi, M., Cattaneo, C.: New method for height estimation of subjects represented in photograms taken from video surveillance systems. Int. J. Legal Med. 121(6), 489–492 (2007)CrossRefGoogle Scholar
  9. 9.
    O’Gorman, L., Yang, G.: Orthographic perspective mappings for consistent wide-area motion feature maps from multiple cameras. IEEE Trans. Image Process. 25(6), 2817–2832 (2016)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Richardson, E., Peleg, S., Werman, M.: Scene geometry from moving objects. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 13–18. IEEE (2014)Google Scholar
  11. 11.
    McConnell, R., Kwok, R., Curlander, J.C., Kober, W., Pang, S.S.: \(\uppsi \)-s correlation and dynamic time warping: two methods for tracking ice floes in SAR images. IEEE Trans. Geosci. Remote Sens. 29(6), 1004–1012 (1991)CrossRefGoogle Scholar
  12. 12.
    Mingqiang, Y., Kidiyo, K., Joseph, R.: A Survey of Shape Feature Extraction Techniques. INTECH Open Access Publisher (2008)Google Scholar
  13. 13.
    Yu, S., Tan, D., Tan, T.: A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 4, pp. 441–444. IEEE (2006)Google Scholar
  14. 14.
    Centro de estudios de población y desarrollo, Cálculos de peso y talla promedio de la población por provincias y Cuba, Oficina Nacional de Estadística (2008)Google Scholar
  15. 15.
    Shao, J., Zhou, S.K., Chellappa, R.: Robust height estimation of moving objects from uncalibrated videos. IEEE Trans. Image Process. 19(8), 2221–2232 (2010)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Ferryman, J., Shahrokni, A.: Pets2009: dataset and challenge. In: 2009 Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS-Winter), pp. 1–6. IEEE (2009)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Advanced Technologies Application CenterHavanaCuba

Personalised recommendations