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Energy minimization approach for online data association with missing data

Abstract : Data association problem is of crucial importance to improve online target tracking performance in many difficult visual environments. Usually, association effectiveness is based on prior information and observation category. However, some problems can arise when targets are quite similar. Therefore, neither the color nor the shape could be helpful informations to achieve the task of data association. Likewise, problems can also arise when tracking deformable targets, under the constraint of missing data, with complex motions. Such restriction, i.e. the lack in prior information, limit the association performance. To remedy, we propose a novel method for data association, inspired from the evolution of the target dynamic model, and based on a global minimization of an energy vector. The main idea is to measure the absolute geometric accuracy between features. Its parameterless constitutes the main advantage of our energy minimization approach. Only one information, the position, is used as input to our algorithm. We have tested our approach on several sequences to show its effectiveness.
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Contributor : Nathalie Gaudechoux Connect in order to contact the contributor
Submitted on : Wednesday, October 26, 2011 - 2:51:57 PM
Last modification on : Friday, January 21, 2022 - 3:18:53 AM


  • HAL Id : inria-00636018, version 1


Abir El Abed, Séverine Dubuisson, Dominique Béréziat. Energy minimization approach for online data association with missing data. VISAPP 2007 - 2nd International Conference on Computer Vision Theory and Applications, Mar 2007, Barcelone, Spain. pp.371-378. ⟨inria-00636018⟩



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