Detection-by-Localization:
Maintenance-Free Change Object Detector
Abstract-Recent researches demonstrate that self-localization
performance is a very useful measure of likelihood-of-change (LoC) for change
detection. In this paper, this "detection-by-localization" scheme is
studied in a novel generalized task of object-level change detection. In our
framework, a given query image is segmented into object-level subimages (termed
"scene parts"), which are then converted to subimage-level pixel-wise
LoC maps via the detection-by-localization scheme. Our approach models a
self-localization system as a ranking function, outputting a ranked list of
reference images, without requiring relevance score. Thanks to this new
setting, we can generalize our approach to a broad class of self-localization
systems. Our ranking based self-localization model allows to fuse
self-localization results from different modalities via an unsupervised rank
fusion derived from a field of multi-modal information retrieval (MMR).
Members: Tanaka Kanji
Relevant Publication:
Tanaka Kanji
Detection-by-Localization: Maintenance-Free Change Object Detector
Bibtex
source, Document PDF
Acknowledgements: This work is supported in part by JSPS KAKENHI
Grant-in-Aid for Young Scientists (B) 23700229, and for Scientific Research (C)
26330297.
Dataset annotation for cross-season change detection
DOWNLOAD: cd19_annotation.txt
Description of file:
meaning
of each line:
query_season reference_seson query_image reference_image num_bbs bb_param
{bb_param}
meaning
of each column:
query_season: season of the query image \in {20120122, 20120331, 20120804,
20121117}
reference_season: season of the reference image (GPS paired with the query
image) \in {20120122, 20120331, 20120804, 20121117}
query_image: ID of the query image (16 digits assigned by the NCLT dataset)
reference_image: ID of the reference image (16 digits assigned by the NCLT
dataset)
num_bbs: number of changed objects (= number of bounding boxes)
bb_param: parameter of the bounding box, bx by ex ey