Advertisement

Convolutional Channel Features-Based Person Identification for Person Following Robots

  • Kenji KoideEmail author
  • Jun Miura
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 867)

Abstract

This paper describes a novel person identification framework for mobile robots. In this framework, we combine Convolutional Channel Features (CCF) and online boosting to construct a classifier of a target person to be followed. It allows us to take advantage of deep neural network-based feature representation and adapt the person classifier to the specific target person depending on circumstances. Through evaluations, we validated that the proposed method outperforms existing person identification methods for mobile robots. We applied the proposed method to a real person following robot, and it has been shown that CCF-based person identification realizes robust person following.

Keywords

Person tracking Person identification Mobile robot 

References

  1. 1.
    Ahmed, E., Jones, M., Marks, T.K.: An improved deep learning architecture for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3908–3916. IEEE (2015)Google Scholar
  2. 2.
    Alvarez-Santos, V., Pardo, X.M., Iglesias, R., Canedo-Rodriguez, A., Regueiro, C.V.: Feature analysis for human recognition and discrimination: application to a person-following behaviour in a mobile robot. Robot. Auton. Syst. 60(8), 1021–1036 (2012)CrossRefGoogle Scholar
  3. 3.
    Arras, K.O., Mozos, O.M., Burgard, W.: Using boosted features for the detection of people in 2D range data. In: IEEE International Conference on Robotics and Automation, pp. 3402–3407. IEEE (2007)Google Scholar
  4. 4.
    Berdugo, G., Soceanu, O., Moshe, Y., Rudoy, D., Dvir, I.: Object reidentification in real world scenarios across multiple non-overlapping cameras. In: European Signal Processing Conference, pp. 1806–1810 (2010)Google Scholar
  5. 5.
    Chen, B.X., Sahdev, R., Tsotsos, J.K.: Integrating stereo vision with a CNN tracker for a person-following robot. In: Lecture Notes in Computer Science, pp. 300–313. Springer (2017)Google Scholar
  6. 6.
    Chen, B.X., Sahdev, R., Tsotsos, J.K.: Person following robot using selected online ada-boosting with stereo camera. In: Conference on Computer and Robot Vision, pp. 48–55 (2017)Google Scholar
  7. 7.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  8. 8.
    Grabner, H., Bischof, H.: On-line boosting and vision. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 260–267. IEEE (2006)Google Scholar
  9. 9.
    Koide, K., Miura, J.: Identification of a specific person using color, height, and gait features for a person following robot. Robot. Auton. Syst. 84, 76–87 (2016)CrossRefGoogle Scholar
  10. 10.
    Leigh, A., Pineau, J., Olmedo, N., Zhang, H.: Person tracking and following with 2D laser scanners. In: IEEE International Conference on Robotics and Automation, pp. 726–733 (2015)Google Scholar
  11. 11.
    Li, W., Zhao, R., Wang, X.: Human reidentification with transferred metric learning. In: Asian Conference on Computer Vision (2012)Google Scholar
  12. 12.
    Li, W., Zhao, R., Xiao, T., Wang, X.: DeepReID: deep filter pairing neural network for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition (2014)Google Scholar
  13. 13.
    Luber, M., Spinello, L., Arras, K.O.: People tracking in RGB-d data with on-line boosted target models. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3844–3849. IEEE (2011)Google Scholar
  14. 14.
    Makihara, Y., Mannami, H., Yagi, Y.: Gait analysis of gender and age using a large-scale multi-view gait database. In: Asian Conference on Computer Vision, pp. 440–451. Springer (2011)Google Scholar
  15. 15.
    Munaro, M., Ghidoni, S., Dizmen, D.T., Menegatti, E.: A feature-based approach to people re-identification using skeleton keypoints. In: In: IEEE International Conference on Robotics and Automation, pp. 5644–5651. IEEE (2014)Google Scholar
  16. 16.
    Munaro, M., Menegatti, E.: Fast RGB-d people tracking for service robots. Auton. Robot. 37(3), 227–242 (2014)CrossRefGoogle Scholar
  17. 17.
    Radosavljevic, Z.: A study of a target tracking method using global nearest neighbor algorithm. Vojnotehnicki glasnik 2, 160–167 (2006)CrossRefGoogle Scholar
  18. 18.
    Sahoo, D., Pham, Q., Lu, J., Hoi, S.C.H.: Online deep learning: Learning deep neural networks on the fly. CoRR abs/1711.03705 (2017). arXiv:1711.03705
  19. 19.
    Satake, J., Chiba, M., Miura, J.: A SIFT-based person identification using a distance-dependent appearance model for a person following robot. In: IEEE International Conference on Robotics and Biomimetics, pp. 962–967. IEEE (2012)Google Scholar
  20. 20.
    Schumann, A., Stiefelhagen, R.: Person re-identification by deep learning attribute-complementary information. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops. IEEE (2017)Google Scholar
  21. 21.
    Yang, B., Yan, J., Lei, Z., Li, S.Z.: Convolutional channel features. In: IEEE International Conference on Computer Vision. IEEE (2015)Google Scholar
  22. 22.
    Zainudin, Z., Kodagoda, S., Dissanayake, G.: Torso detection and tracking using a 2D laser range finder. In: Australasian Conference on Robotics and Automation, ARAA (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Toyohashi University of TechnologyToyohashiJapan

Personalised recommendations