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Salient objects detection in dynamic scenes using color and texture features

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Abstract

Visual saliency is an important research topic in the field of computer vision due to its numerous possible applications. It helps to focus on regions of interest instead of processing the whole image or video data. Detecting visual saliency in still images has been widely addressed in literature with several formulations. However, visual saliency detection in videos has attracted little attention, and is a more challenging task due to additional temporal information. A common approach for obtaining a spatio-temporal saliency map is to combine a static saliency map and a dynamic saliency map. In our work, we model the dynamic textures in a dynamic scene with local binary patterns to compute the dynamic saliency map, and we use color features to compute the static saliency map. Both saliency maps are computed using a bio-inspired mechanism of human visual system with a discriminant formulation known as center surround saliency, and are fused in a proper way. The proposed model has been extensively evaluated with diverse publicly available datasets which contain several videos of dynamic scenes, and comparison with state-of-the art methods shows that it achieves competitive results.

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Authors and Affiliations

  1. Université de Bourgogne - France Comté; Le2i - UMR CNRS, 6306, Bourgogne, France

    Satya M. Muddamsetty, Désiré Sidibé & Fabrice Mériaudeau

  2. Laboratoire Hubert Curien UMR CNRS, Université Jean Monnet, 5116, St-Etienne, France

    Alain Trémeau

  3. Center for Intelligent Signal and Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi, Petronas, Malaysia

    Fabrice Mériaudeau

Authors
  1. Satya M. Muddamsetty
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  2. Désiré Sidibé
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  3. Alain Trémeau
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  4. Fabrice Mériaudeau
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Correspondence to Désiré Sidibé.

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Muddamsetty, S.M., Sidibé, D., Trémeau, A. et al. Salient objects detection in dynamic scenes using color and texture features. Multimed Tools Appl 77, 5461–5474 (2018). https://doi.org/10.1007/s11042-017-4462-y

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  • DOI: https://doi.org/10.1007/s11042-017-4462-y

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