Adaptive Feature Selection for Object Tracking with Particle Filter

Abstract : Object tracking is an important topic in the field of computer vision. Commonly used color-based trackers are based on a fixed set of color features such as RGB or HSV and, as a result, fail to adapt to changing illumination conditions and background clutter. These drawbacks can be overcome to an extent by using an adaptive framework which selects for each frame of a sequence the features that best discriminate the object from the background. In this paper, we use such an adaptive feature selection method embedded into a particle filter mechanism and show that our tracking method is robust to lighting changes and background distractions. Different experiments also show that the proposed method outperform other approaches.
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Communication dans un congrès
International Conference on Image Analysis and Recognition, Oct 2014, Vilamoura, Algarve, Portugal. pp.395-402, 2014, ICIAR. <10.1007/978-3-319-11755-3_44>
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Contributeur : Désiré Sidibé <>
Soumis le : vendredi 24 avril 2015 - 18:28:06
Dernière modification le : lundi 12 septembre 2016 - 15:30:06

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Darshan Venkatrayappa, Désiré Sidibé, Fabrice Meriaudeau, Philippe Montesinos. Adaptive Feature Selection for Object Tracking with Particle Filter. International Conference on Image Analysis and Recognition, Oct 2014, Vilamoura, Algarve, Portugal. pp.395-402, 2014, ICIAR. <10.1007/978-3-319-11755-3_44>. <hal-01145635>

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