A Performance Evaluation of Fusion Techniques for Spatio-Temporal Saliency Detection in Dynamic Scenes

Satya Muddamsetty (1) , Désiré Sidibé (1) , Alain Trémeau (2) , Fabrice Mériaudeau (1)

1 Le2i - Laboratoire Electronique, Informatique et Image [UMR6306]
2 LHC - Laboratoire Hubert Curien [Saint Etienne]

Visual saliency is an important research topic in computer vision applications, which helps to focus on regions of interest instead of processing the whole image. Detecting visual saliency in still images has been widely addressed in literature. However, visual saliency detection in videos is more complicated due to additional temporal information. A spatio-temporal saliency map is usually obtained by the fusion of a static saliency map and a dynamic saliency map. The way both maps are fused plays a critical role in the accuracy of the spatio-temporal saliency map. In this paper, we evaluate the performances of different fusion techniques on a large and diverse dataset and the results show that a fusion method must be selected depending on the characteristics, in terms of color and motion contrasts, of a sequence. Overall, fusion techniques which take the best of each saliency map (static and dynamic) in the final spatio-temporal map achieve best results.

Mots clés: Visual salincy; Spatio-temporal saliency; context information; fusion; performance evaluation

Domaines: Informatique [cs]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Satya Muddamsetty, Désiré Sidibé, Alain Trémeau, Fabrice Mériaudeau. A Performance Evaluation of Fusion Techniques for Spatio-Temporal Saliency Detection in Dynamic Scenes. IEEE International Conference on Image Processing, Sep 2013, Melbourne, Australia. pp.1-5. ⟨hal-00835782⟩