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Joint Tracking and Segmentation of Objects using Graph Cuts.

Aurélie Bugeau 1 Patrick Pérez 1
1 VISTA - Vision spatio-temporelle et active
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : This paper presents a new method to both track and segment objects in videos. It includes predictions and observations inside an energy function that is minimized with graph cuts. The min-cut/max-flow algorithm provides a segmentation as the global minimum of the energy function, at a modest computational cost. Simultaneously, our algorithm associates the tracked objects to the observations during the tracking. It thus combines “detect-before-track” tracking algorithms and segmentation methods based on color/motion distributions and/or temporal consistency. Results on real sequences are presented in which the robustness to partial occlusions and to missing observations is shown.
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Contributor : Aurélie Bugeau <>
Submitted on : Tuesday, January 4, 2011 - 10:35:15 AM
Last modification on : Thursday, March 25, 2021 - 3:31:36 AM
Long-term archiving on: : Tuesday, April 5, 2011 - 2:45:43 AM


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  • HAL Id : hal-00551593, version 1


Aurélie Bugeau, Patrick Pérez. Joint Tracking and Segmentation of Objects using Graph Cuts.. ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems, 2007, Netherlands. ⟨hal-00551593⟩



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