Motion estimation on infrared image sequences

Aerial surveillance is an issue of key importance for warship protection. In addition to radar systems, infrared surveillance sensors represent an interesting alternative for remote observation. In this paper, we work on images providing by such a system and we propose an original approach to the tracking of complex patterns in noisy infrared image sequences. We have paid particular attention to robustness with regards to perturbations likely to occur (noise, 'lining effects'. . .). Our method relies on robust parametric motion estimation and on an original Markovian regularization scheme allows to handle with the appearance and the disappearance of objects in the scene. Numerous experiments performed on outdoor infrared image sequences underline the efficiency of the proposed method.

[1]  Patrick Bouthemy,et al.  A region-level graph labeling approach to motion-based segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Ali Tabatabai,et al.  Motion Estimation Methods for Video Compression—A Review , 1998 .

[3]  Rémi Ronfard,et al.  Modèles de Potts et relaxation d'images de labels par champs de Markov , 1992 .

[4]  J. Besag.  On the Statistical Analysis of Dirty Pictures , 1986 .

[5]  Edward H. Adelson,et al.  A unified mixture framework for motion segmentation: incorporating spatial coherence and estimating the number of models , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Jean-Marc Odobez,et al.  Robust Multiresolution Estimation of Parametric Motion Models , 1995, J. Vis. Commun. Image Represent..

[7]  Patrick Pérez,et al.  Dense estimation and object-based segmentation of the optical flow with robust techniques , 1998, IEEE Trans. Image Process..

[8]  C. Collet,et al.  Active contour models for infrared cloudy shapes segmentation , 1994, Proceedings of OCEANS'94.

[9]  A. D. Brink,et al.  Grey-level thresholding of images using a correlation criterion , 1989, Pattern Recognit. Lett..

[10]  Sushil K. Bhattacharjee,et al.  Robust region merging for spatio-temporal segmentation , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[11]  Andrew Blake,et al.  Affine-invariant contour tracking with automatic control of spatiotemporal scale , 1993, 1993 (4th) International Conference on Computer Vision.

[12]  Rachid Deriche,et al.  Region tracking through image sequences , 1995, Proceedings of IEEE International Conference on Computer Vision.

[13]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[14]  Patrick Bouthemy,et al.  Multimodal Estimation of Discontinuous Optical Flow using Markov Random Fields , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Michal Irani,et al.  Detecting and Tracking Multiple Moving Objects Using Temporal Integration , 1992, ECCV.

[16]  André Quinquis,et al.  Cloudy Sky Velocity Map Based on Matched Filter , 1992, MVA.

[17]  Christophe Collet,et al.  Cloudy sky velocity estimation based on optical flow estimation leading with an entropy criterion , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,.

[18]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[19]  Jean-Marc Odobez,et al.  Direct incremental model-based image motion segmentation for video analysis , 1998, Signal Process..