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Fourier Features For Person Detection in Depth Data

  • Viktor Seib
  • Guido Schmidt
  • Michael Kusenbach
  • Dietrich Paulus
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9256)

Abstract

A robust and reliable person detection is crucial for many applications. In the domain of service robots that we focus on, knowing the location of a person is an essential requirement for any meaningful human-robot interaction. In this work we present a people detection algorithm exploiting RGB-D data from Kinect-like cameras. Two features are obtained from the data representing the geometrical properties of a person. These features are transformed into the frequency domain using Discrete Fourier Transform (DFT) and used to train a Support Vector Machine (SVM) for classification. Additionally, we present a hand detection algorithm based on the extracted silhouette of a person. We evaluate the proposed method on real world data from the Cornell Activity Dataset and on a dataset created in our laboratory.

Keywords

People detection Silhouette detection Hand detection Fourier features Service robots 

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References

  1. 1.
    Bertozzi, M., Broggi, A., Del Rose, M., Felisa, M., Rakotomamonjy, A., Suard, F.: A pedestrian detector using histograms of oriented gradients and a support vector machine classifier. In: Intelligent Transportation Systems Conference, ITSC 2007, pp. 143–148. IEEE (2007)Google Scholar
  2. 2.
    Brown, L.G.: A survey of image registration techniques. ACM Computing Surveys (CSUR) 24(4), 325–376 (1992)CrossRefGoogle Scholar
  3. 3.
    Cerlinca, T.L., Pentiuc, S.G., Vatavu, R.D., Cerlinca, M.C.: Hand posture recognition for human-robot interaction. In: Proceedings of the 2007 Workshop on Multimodal Interfaces in Semantic Interaction, pp. 47–50. ACM (2007)Google Scholar
  4. 4.
    Choi, W., Pantofaru, C., Savarese, S.: Detecting and tracking people using an rgb-d camera via multiple detector fusion. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1076–1083. IEEE (2011)Google Scholar
  5. 5.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893 (June 2005)Google Scholar
  6. 6.
    Ghosh, S., Zheng, J., Chen, W., Zhang, J., Cai, Y.: Real-time 3d markerless multiple hand detection and tracking for human computer interaction applications. In: Proceedings of the 9th ACM SIGGRAPH Conference on Virtual-Reality Continuum and its Applications in Industry, pp. 323–330. ACM (2010)Google Scholar
  7. 7.
    González, D.I.R., Hayet, J.-B.: Fast Human Detection in RGB-D Images with Progressive SVM-Classification. In: Klette, R., Rivera, M., Satoh, S. (eds.) PSIVT 2013. LNCS, vol. 8333, pp. 337–348. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  8. 8.
    Hordern, D., Kirchner, N.: Robust and efficient people detection with 3-d range data using shape matching. In: Australasian Conference on Robotics and Automation (2010)Google Scholar
  9. 9.
    Kakumanu, P., Makrogiannis, S., Bourbakis, N.: A survey of skin-color modeling and detection methods. Pattern Recognition 40(3), 1106–1122 (2007)CrossRefzbMATHGoogle Scholar
  10. 10.
    Lee, S.J., Nguyen, D.D., Jeon, J.W.: Design and Implementation of Depth Image Based Real-Time Human Detection. Journal of Semiconductor Technology and Science 14(2), 212–226 (2014)Google Scholar
  11. 11.
    Xia, L., Chen, C.-C., Aggarwal, J.K.: Human Detection Using Depth Information by Kinect. In: International Workshop on Human Activity Understanding from 3D Data in Conjunction with CVPR (HAU3D) (2011)Google Scholar
  12. 12.
    Paisitkriangkrai, S., Shen, C., van den Hengel, A.: Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features. CoRR, abs/1407.0786 (2014)Google Scholar
  13. 13.
    Spinello, L., Arras, K.O.: People detection in RGB-D data. In: IEEE/RSJ Int. Conf. on (2011)Google Scholar
  14. 14.
    Sung, J., Ponce, C., Selman, B., Saxena, A.: Human activity detection from rgbd images. plan, activity, and intent recognition, 64 (2011)Google Scholar
  15. 15.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(11), 1330–1334 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Viktor Seib
    • 1
  • Guido Schmidt
    • 1
  • Michael Kusenbach
    • 1
  • Dietrich Paulus
    • 1
  1. 1.Active Vision Group (AGAS)University of Koblenz-LandauKoblenzGermany

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