Drone-View Building Identification by Cross-View Visual Learning and Relative Spatial Estimation

Drones become popular recently and equip more sensors than traditional cameras, which bring emerging applications and research. To enable drone-based applications, providing related information (e.g., building) to understand the environment around the drone is essential. We frame this drone-view bui...

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Bibliographic Details
Published in2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) pp. 1558 - 15588
Main Authors Chen, Chun-Wei, Kuo, Yin-Hsi, Lee, Tang, Lee, Cheng-Han, Hsu, Winston
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2018
Subjects
Buildings
Drones
Geology
Google
Proposals
Sensors
Visualization
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Summary:Drones become popular recently and equip more sensors than traditional cameras, which bring emerging applications and research. To enable drone-based applications, providing related information (e.g., building) to understand the environment around the drone is essential. We frame this drone-view building identification as building retrieval problem: given a building (multimodal query) with its images, geolocation and drone's current location, we aim to retrieve the most likely proposal (building candidate) on a drone-view image. Despite few annotated drone-view images to date, there are many images of other views from the Web, like ground-level, street-view and aerial images. Thus, we propose a cross-view triplet neural network to learn visual similarity between drone-view and other views, further design relative spatial estimation of each proposal and the drone, and collect new drone-view datasets for the task. Our method outperforms triplet neural network by 0.12 mAP. (i.e., 22.9 to 35.0, +53% in a sub-dataset [LA])
ISSN:2160-7516
DOI:10.1109/CVPRW.2018.00197