Probabilistic Matching Algorithm for Keypoint Based Object Tracking Using a Delaunay Triangulation

This article presents a matching algorithm developed for a generic object tracking system. Matching is a critical part for the effectiveness of tracking. The proposed method is a probabilistic algorithm inspired from the emerging "discriminative random fields". Points are associated according to their visual similarity and to spatial relations in their neighborhood, based on a Delaunay triangulation. Experimental results are presented to validate this contribution.

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