Advertisement

Fish-Eye Camera Video Processing and Trajectory Estimation Using 3D Human Models

  • Konstantina Kottari
  • Kostas Delibasis
  • Vassilis Plagianakos
  • Ilias Maglogiannis
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 436)

Abstract

Video processing and analysis applications are part of Artificial Intelligence. Frequently, silhouettes in video frames lack depth information, especially in case of a single camera. In this work, we utilize a three-dimensional human body model, combined with a calibrated fish-eye camera, to obtain three-dimensional (3D) clues. More specifically, a generic 3D human model in various poses is derived from a novel mathematical formalization of a well-known class of geometric primitives, namely the generalized cylinders, which exhibit advantages over the existing parametric definitions. The use of the fish-eye camera allows the generation of rendered silhouettes, using these 3D models. Moreover, we present a very efficient algorithm for matching that 3D model with a real human figure in order to recognize the posture of a monitored person. Firstly, the silhouette is segmented in each frame and the calculation of the real human position is calculated. Subsequently, an optimization process adjusts the parameters of the 3D human model in an attempt to match the pose (position and orientation relatively to the camera) of real human. The experimental results are promising, since the pose, the trajectory and the orientation of the human can be accurately estimated.

Keywords

fish-eye camera video processing three-dimensional human modelling posture recognition minimization generalized cylinders and elliptical intersections 

References

  1. 1.
    Bottino, A., Laurentini, A.: A silhouette-based technique for the reconstruction of human movement. Computer Vision and Image Understanding (CVIU) 83(1), 79–95 (2001)CrossRefzbMATHGoogle Scholar
  2. 2.
    Cheung, G.K.M., Baker, S., Kanade, T.: Shape-from silhouette of articulated objects and its use for human body kinematics estimation and motion capture. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR 2003), Madison, WI, vol. 1, pp. 77–84 (2003)Google Scholar
  3. 3.
    Mikic, I., Trivedi, M., Hunter, E., Cosman, P.: Human body model acquisition and tracking using voxel data. International Journal of Computer Vision 53(3), 199–223 (2003)CrossRefGoogle Scholar
  4. 4.
    Plänkers, R., Fua, P.: Tracking and modeling people in video sequences. Computer Vision and Image Understanding (CVIU) 81(3), 285–302 (2001)CrossRefzbMATHGoogle Scholar
  5. 5.
    Haritaoglu, I., Harwood, D., Davis, L.S.: W4S: A real-time system for detecting and tracking people in \(2\frac{1}{2}D\). In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 877–892. Springer, Heidelberg (1998)Google Scholar
  6. 6.
    Jojic, N., Gu, J., Shen, H.: S. Huang T.S: 3-Dreconstruction of multipart, self-occluding objects. In: Proceedingsof the Asian Conference on Computer Vision (ACCV 1998), HongKong, China, pp. 455–462 (1998)Google Scholar
  7. 7.
    Delibasis, K.K., Goudas, T., Plagianakos, V.P., Maglogiannis, I.: Fisheye Camera Modeling for Human Segmentation Refinement in Indoor Videos. In: PETRA 2013, Island of Rhodes, Greece, May 29-31 (2013), Copyright 2013 ACM 978-1-4503-1973-7/13/05Google Scholar
  8. 8.
    Delibasis, K.K., Kechriniotis, A., Maglogiannis, I.: A novel tool for segmenting 3D medical images based on generalized cylinders and active surfaces. Computer Methods and Programs in Biomedicine 111, 148–165 (2013)CrossRefGoogle Scholar
  9. 9.
  10. 10.
    Nelder, J.A., Mead, R.: A simplex method for function minimization. Computer Journal 7, 308–313 (1965)CrossRefzbMATHGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • Konstantina Kottari
    • 1
  • Kostas Delibasis
    • 1
  • Vassilis Plagianakos
    • 1
  • Ilias Maglogiannis
    • 2
  1. 1.Dept. of Computer Science and Biomedical InformaticsUniversity of ThessalyLamiaGreece
  2. 2.Dept. of Digital SystemsUniversity of PiraeusPiraeusGreece

Personalised recommendations