Optic flow estimation by support vector regression

Authors:
Johan Colliez;Franck Dufrenois;Denis Hamad
Affiliations:
Université du Littoral Côte d'Opale, Laboratoire d'Analyse des Systèmes du Littoral, 50 rue Ferdinand Buisson, BP 699, 62228 Calais, Cedex, France;Université du Littoral Côte d'Opale, Laboratoire d'Analyse des Systèmes du Littoral, 50 rue Ferdinand Buisson, BP 699, 62228 Calais, Cedex, France;Université du Littoral Côte d'Opale, Laboratoire d'Analyse des Systèmes du Littoral, 50 rue Ferdinand Buisson, BP 699, 62228 Calais, Cedex, France
Venue:
Engineering Applications of Artificial Intelligence
Year:
2006

Citing 6
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Abstract

In this paper, we describe an approach to estimate optic flow from an image sequence based on Support Vector Regression (SVR) machines with an adaptive @?-margin. This approach uses affine and constant models for velocity vectors. Synthetic and real image sequences are used in order to compare results of the SVR approach against other well-known optic flow estimation methods. Experimental results on real traffic sequences show that SVR approach is an appropriate solution for object tracking.