Utilizing high-level visual feature for indoor shopping mall localization
Towards robust and convenient indoor shopping mall localization, we propose a novel learning-based scheme to utilize the high-level visual information from the storefront images captured by personal devices of users for shop recognition tasks. Specifically, given a storefront input image, the novel feature fusion scheme (denoted as FusionNet) fuses the distinguishing DNN-based appearance feature and text feature for robust recognition of store brands. We further utilize this scheme for localization by matching shop recognition results to the topological map constructed from the indicator map. Experiments on storefront image dataset show that the proposed recognition scheme outperforms existing ones by a large margin. Further experiments performed in real shopping malls demonstrate that our scheme is applicable to accurate localization tasks.