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http://hdl.handle.net/1942/23242
Title: | No more Autobahn! Scenic Route Generation Using Googles Street View | Authors: | Runge, Nina SAMSONOV, Pavel DEGRAEN, Donald SCHOENING, Johannes |
Issue Date: | 2016 | Publisher: | ASSOC COMPUTING MACHINERY | Source: | IUI '16 Proceedings of the 21st International Conference on Intelligent User Interfaces, ASSOC COMPUTING MACHINERY,p. 147-151 | Abstract: | Navigation systems allow drivers to find the shortest or fastest path between two or multiple locations mostly using time or distance as input parameters. Various researchers extended traditional route planning approaches by taking into account the user's preferences, such as enjoying a coastal view or alpine landscapes during a drive. Current approaches mainly rely on volunteered geographic information (VGI), such as point of interest (POI) data from OpenStreetMap, or social media data, such as geotagged photos from Flickr, to generate scenic routes. While these approaches use proximity, distribution or other spatial relationships of the data sets, they do not take into account the actual view on specific route segments. In this paper, we propose Autobahn: a system for generating scenic routes using Google Street View images to classify route segments based on their visual characteristics enhancing the driving experience. We show that this vision-based approach can complement other approaches for scenic route planning and introduce a personalized scenic route by aligning the characteristics of the route to the preferences of the user. | Notes: | [Runge, Nina] Univ Bremen, TZI, Digital Media Lab, Bibliotheksstr 1, D-28359 Bremen, Germany. [Samsonov, Pavel; Degraen, Donald; Schoening, Johannes] Hasselt Univ TUL IMinds, Expertise Ctr Digital Media EDM, Wetenschapspk 2, B-3590 Diepenbeek, Belgium. | Keywords: | deep learning; intelligent user interfaces; Google Street View; scenic routes;Deep Learning; Intelligent User Interfaces; Google Street View; Scenic Routes | Document URI: | http://hdl.handle.net/1942/23242 | ISBN: | 9781450341370 | DOI: | 10.1145/2856767.2856804 | ISI #: | 000389809600018 | Rights: | Copyright © 2016 by the Association for Computing Machinery, Inc. (ACM). Permission to make digital or hard copies of portions of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyright for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permission to republish from: permissions@acm.org or Fax +1 (212) 869-0481. | Category: | C1 | Type: | Proceedings Paper | Validations: | ecoom 2018 |
Appears in Collections: | Research publications |
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p147-runge.pdf | Published version | 4.69 MB | Adobe PDF | View/Open |
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