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JRM Vol.31 No.2 pp. 221-230
doi: 10.20965/jrm.2019.p0221
(2019)

Paper:

Cross-Domain Change Object Detection Using Generative Adversarial Networks

Takuma Sugimoto, Kanji Tanaka, and Kousuke Yamaguchi

University of Fukui
3-9-1 Bunkyo, Fukui-shi, Fukui 910-8507, Japan

Received:
October 22, 2018
Accepted:
February 6, 2019
Published:
April 20, 2019
Keywords:
long-term map maintenance, cross-season change object detection, generative adversarial networks
Abstract
Cross-Domain Change Object Detection Using Generative Adversarial Networks

Cross-domain change object detection

Image change detection is a fundamental problem for robotic map maintenance and long-term map learning. Local feature-based image comparison is one of the most basic schemes for addressing this problem. However, the local-feature approach encounters difficulties when the query and reference images involve different domains (e.g., time of the day, weather, season). In this paper, we address the local-feature approach from the novel perspective of object-level region features. This study is inspired by the recent success of object-level region features in cross-domain visual place recognition (CD-VPR). Unlike the previous contributions of the CD-VPR task, in the cross-domain change detection (CD-CD) tasks, we consider matching a small part (i.e., the change) of the scene and not the entire image, which is considerably more demanding. To address this issue, we explore the use of two independent object proposal techniques: supervised object proposal (e.g., YOLO) and unsupervised object proposal (e.g., BING). We combine these techniques and compute appearance features of their arbitrarily shaped objects by aggregating local features from a deep convolutional neural network (DCN). Experiments using a publicly available cross-season NCLT dataset validate the efficacy of the proposed approach.

Cite this article as:
T. Sugimoto, K. Tanaka, and K. Yamaguchi, “Cross-Domain Change Object Detection Using Generative Adversarial Networks,” J. Robot. Mechatron., Vol.31, No.2, pp. 221-230, 2019.
Data files:
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Last updated on Sep. 19, 2019