Motion Direction Detection from Segmentation by LIAC, and Tracking by Centroid Trajectory Calculation

Antonio Fernández-Caballero

Abstract

Motion information can form the basis of predictions about time-to-impact and the trajectories of objects moving through a scene. Firstly, a model that incorporates accumulative computation and lateral interaction is presented. By means of the lateral interaction in accumulative computation (LIAC) of each element with its neighbours, the model is able to segment moving objects present in an indefinite sequence of images. In a further step, moving objects are tracked using a centroid-based trajectory calculation.

References

  1. D. Hogg, “Model-based vision: A program to see a walking person”, Image and Vision Computing, vol. 1, no. 1, pp. 5-20, 1983.
  2. J.L. Barron, D.J. Fleet, S.S. Beauchemin, “Performance of optical flow techniques”, International Journal of Computer Vision, vol. 12, no. 1, pp. 43-77, 1994.
  3. R. Jain, W.N. Martin, J.K. Aggarwal, “Segmentation through the detection of changes due to motion”, Computer Graphics and Image Processing, 11, pp. 13-34, 1970.
  4. M.A. Fernández, J. Mira, “Permanence memory: A system for real time motion analysis in image sequences”, in IAPR Workshop on Machine Vision Applications, MVA'92, 1992, pp. 249-252.
  5. T.S. Huang, A.N. Netravali, “Motion and structure from feature correspondences: A review”, Proceedings of the IEEE, 82, pp. 252-269, 1994.
  6. R. Deriche, O.D. Faugeras, “Tracking line segments”, Image and Vision Computing, vol. 8, no. 4, pp. 261-270, 1990.
  7. A. Fernández-Caballero, Jose Mira, Ana E. Delgado, M.A. Fernández, “Lateral interaction in accumulative computation: A model for motion detection”, Neurocomputing, 50C, 2003, pp. 341-364.
  8. A. Fernández-Caballero, J. Mira, M.A. Fernández, A.E. Delgado, “On motion detection through a multi-layer neural network architecture”, Neural Networks, 16 (2), 2003, pp. 205-222.
  9. M. Gelgon, P. Bouthemy, “A region-level motion-based graph representation and labeling for tracking a spatial image partition,” Pattern Recognition, 33 (4), 2000, pp. 725-740.
  10. G. Liu, R. M. Haralick, “Using centroid covariance in target recognition,” Proceedings ICPR98, 1998, pp. 1343-1346.
Download


Paper Citation


in Harvard Style

Fernández-Caballero A. (2005). Motion Direction Detection from Segmentation by LIAC, and Tracking by Centroid Trajectory Calculation . In Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2005) ISBN 972-8865-28-7, pages 213-218. DOI: 10.5220/0002570102130218


in Bibtex Style

@conference{pris05,
author={Antonio Fernández-Caballero},
title={Motion Direction Detection from Segmentation by LIAC, and Tracking by Centroid Trajectory Calculation},
booktitle={Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2005)},
year={2005},
pages={213-218},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002570102130218},
isbn={972-8865-28-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2005)
TI - Motion Direction Detection from Segmentation by LIAC, and Tracking by Centroid Trajectory Calculation
SN - 972-8865-28-7
AU - Fernández-Caballero A.
PY - 2005
SP - 213
EP - 218
DO - 10.5220/0002570102130218