Improving a vision indoor localization system by a saliency-guided detection

In this paper, we propose to use visual saliency to improve an indoor localization system based on image matching. A learning step permits to determinate the reference trajectory by selecting some key frames along the path. During the localization step, the current image is then compared to the obtained key frames in order to estimate the user's position. This comparison is realized by extracting primitive information through a saliency method, which aims to improve our localization system by focusing our attention on the more singular regions to match. Another advantage of the saliency-guided detection is to save computation time. The proposed framework has been developed and tested on a Smartphone. The obtained results show the interest of the use of saliency models by comparing the numbers of features and good matches in video sequence.