ROBUST MOTION SEGMENTATION FOR HIGH DEFINITION VIDEO SEQUENCES USING A FAST MULTI-RESOLUTION MOTION ESTIMATION BASED ON SPATIO-TEMPORAL TUBES

Conference object English OPEN
Brouard , Olivier ; Delannay , Fabrice ; Ricordel , Vincent ; Barba , Dominique (2007)
  • Publisher: HAL CCSD
  • Subject: Robust Motion Segmentation | [ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing | Spatio- Temporal Tubes | Multi-Resolution Motion Estimation | Global Mo-tion Estimation | Global Motion Estimation | [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing | Spatio-Temporal Tubes
    acm: ComputingMethodologies_COMPUTERGRAPHICS | ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION

4 pages; International audience; Motion segmentation methods are effective for tracking video objects. However, objects segmentation methods based on motion need to know the global motion of the video in order to back-compensate it before computing the segmentation. In this paper, we propose a method which estimates the global motion of a High Definition (HD) video shot and then segments it using the remaining motion information. First, we develop a fast method for multi-resolution motion estimation based on spatio-temporal tubes. So we get a homogeneous motion vectors field (one vector per tube). From this motion field, we use a robust approach to estimate the parameters of the affine model that characterizes the global motion of the shot. After back-compensation of the video shot global motion, the remaining motion vectors are used to achieve the motion segmentation and extract the video objects.
  • References (10)

    [1] R. Megret and D. DeMenthon, “A survey of spatio-temporal grouping techniques,” Tech. Rep., LAMP-TR-094/CS-TR4403, University of Maryland, 1994.

    [2] F. Porikli and Y. Wang, “Automatic Video Object Segmentation Using Volume Growing and Hierarchical Clustering,” vol. 3, pp. 442 - 453, March 2004.

    [3] Y. Wang, J. F. Doherty, and R. E. Van Dyck, “Moving Object Tracking in Video,” Washington DC, USA, October 2000, Proc. IEEE Applied Imagery Pattern Recognition Workshop.

    [4] B.K.P. Horn and B.G. Schunck, “Determining Optical Flow,” Artificial Intelligence, vol. 17, no. 1 - 3, pp. 185 - 203, 1981.

    [5] J. M. Odobez and P. Bouthemy, “Robust Multiresolution Estimation of Parametric Motion Models,” Journal of Visual Communication and Image Representation, vol. 6, December 1995.

    [6] S. Pe´chard, P. Le Callet, M. Carnec, and D. Barba, “A new methodology to estimate the impact of H.264 artefacts on subjective video quality,” Scottsdale, VPQM 2007.

    [7] R. Coudray and B. Besserer, “Global Motion Estimation for MPEG-Encoded Streams,” Singapore, in Proc. ICIP 2004.

    [8] O. Le Meur, P. Le Callet, and D. Barba, “A Coherent Computational Approach to Model Bottom-Up Visual Attention,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 28, no. 5, pp. 802 - 817, May 2006.

    [9] SVT, “Overall-quality assessment when targeting wide xga flat panel displays,” Tech. Rep., SVT corporate development technology, 2002.

    [10] IRISA, “Motion2D,” http://www.irisa.fr/Vista/Motion2D/.

  • Metrics
    No metrics available
Share - Bookmark