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Communication Dans Un Congrès Année : 2014

Adaptive Feature Selection for Object Tracking with Particle Filter

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Résumé

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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Dates et versions

hal-01145635 , version 1 (24-04-2015)

Identifiants

Citer

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, ⟨10.1007/978-3-319-11755-3_44⟩. ⟨hal-01145635⟩
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