Browse/search for people

Publication - Professor Andrew Calway

    Visual place recognition using landmark distribution descriptors


    Panphattarasap, P & Calway, A, 2017, ‘Visual place recognition using landmark distribution descriptors’. in: Computer Vision - ACCV 2016: 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers. Springer-Verlag Berlin, pp. 487-502


    Recent work by Sünderhauf et al. [1] demonstrated improved visual place recognition using proposal regions coupled with features from convolutional neural networks (CNN) to match landmarks between views. In this work we extend the approach by introducing descriptors built from landmark features which also encode the spatial distribution of the landmarks within a view. Matching descriptors then enforces consistency of the relative positions of landmarks between views. This has a significant impact on performance. For example, in experiments on 10 image-pair datasets, each consisting of 200 urban locations with significant differences in viewing positions and conditions, we recorded average precision of around 70% (at 100% recall), compared with 58% obtained using whole image CNN features and 50% for the method in [1].

    Full details in the University publications repository