Mobile Phone Enabled Museum Guidance with Adaptive Classification

  • Although audio guides are widely established in many museums, they suffer from several drawbacks compared to state-of-the-art multimedia technologies: First, they provide only audible information to museum visitors, while other forms of media presentation, such as reading text or video could be beneficial for museum guidance tasks. Second, they are not very intuitive. Reference numbers have to beAlthough audio guides are widely established in many museums, they suffer from several drawbacks compared to state-of-the-art multimedia technologies: First, they provide only audible information to museum visitors, while other forms of media presentation, such as reading text or video could be beneficial for museum guidance tasks. Second, they are not very intuitive. Reference numbers have to be manually keyed in by the visitor before information about the exhibit is provided. These numbers are either displayed on visible tags that are located near the exhibited objects, or are printed in brochures that have to be carried. Third, offering mobile guidance equipment to visitors leads to acquisition and maintenance costs that have to be covered by the museum. With our project PhoneGuide we aim at solving these problems by enabling the application of conventional camera-equipped mobile phones for museum guidance purposes. The advantages are obvious: First, today’s off-the-shelf mobile phones offer a rich pallet of multimedia functionalities ---ranging from audio (over speaker or head-set) and video (graphics, images, movies) to simple tactile feedback (vibration). Second, integrated cameras, improvements in processor performance and more memory space enable supporting advanced computer vision algorithms. Instead of keying in reference numbers, objects can be recognized automatically by taking non-persistent photographs of them. This is more intuitive and saves museum curators from distributing and maintaining a large number of physical (visible or invisible) tags. Together with a few sensor-equipped reference tags only, computer vision based object recognition allows for the classification of single objects; whereas overlapping signal ranges of object-distinct active tags (such as RFID) would prevent the identification of individuals that are grouped closely together. Third, since we assume that museum visitors will be able to use their own devices, the acquisition and maintenance cost for museum-owned devices decreases.show moreshow less

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Metadaten
Document Type:Report
Author: Erich Bruns, Benjamin Brombach, Prof. Dr. Oliver BimberORCiDGND
DOI (Cite-Link):https://doi.org/10.25643/bauhaus-universitaet.940Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20111215-9406Cite-Link
Language:English
Date of Publication (online):2007/11/16
Year of first Publication:2007
Release Date:2007/11/16
Contributing Corporation:Stiftung für Technologie, Innovation und Forschung Thüringen (STIFT)
Institutes:Fakultät Medien / Juniorprofessur Augmented Reality
Tag:Ad-hoc Sensor-Netzwerke; Adaptive Klassifizierung; Mobiltelefone; Museumsführer
ad-hoc sensor networks; adaptive classification; mobile phones; museum guidance; object recognition
GND Keyword:Objektverfolgung; Neuronales Netz; Handy; Objekterkennung; Museum; Anpassung
Dewey Decimal Classification:700 Künste und Unterhaltung / 700 Künste / 700 Künste; Bildende und angewandte Kunst
BKL-Classification:54 Informatik / 54.72 Künstliche Intelligenz
54 Informatik / 54.74 Maschinelles Sehen
Licence (German):License Logo In Copyright