TY - GEN
T1 - Visual Place Recognition Using Landmark Distribution Descriptors
AU - Panphattarasap, Pilailuck
AU - Calway, Andrew
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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].
AB - 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].
UR - https://www.scopus.com/pages/publications/105036953512
U2 - 10.1007/978-3-319-54190-7 30
DO - 10.1007/978-3-319-54190-7 30
M3 - Conference contribution
AN - SCOPUS:105036953512
SN - 9783319541891
T3 - Lecture Notes in Computer Science
SP - 487
EP - 502
BT - Computer Vision – ACCV 2016 - 13th Asian Conference on Computer Vision, Revised Selected Papers, Part 4
A2 - Lai, Shang-Hong
A2 - Nishino, Ko
A2 - Lepetit, Vincent
A2 - Sato, Yoichi
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th Asian Conference on Computer Vision, ACCV 2016
Y2 - 20 November 2016 through 24 November 2016
ER -