{'fraction_non_alphanumeric': 0.020610057708161583, 'fraction_numerical': 0.0, 'mean_word_length': 5.491978609625669, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Supporting Indoor Navigation by Using Image Recognition Technique on Mobile Devices

Shan-Chih Lin Kuo-Feng Hwang Geeng-Neng You Abstract—With the rapid progress of mobile technology, applications of mobile devices about geographic navigation support to help people move around have been extensively explored during the last decade. However, most of these applications are focused on outdoors. This study presents a novel way to support indoor navigation. We explore various approaches to support indoor navigation and employ an image recognition technology called Scale Invariant Feature Transform (SIFT) to construct a prototype system. The prototype can be easily installed on smartphones for supporting indoor navigation. We did a field test on a real occasion of guiding new meeting attenders around the designated floor of a building. Most users with relevant devices had utilized the system and delivered precious feedbacks. The general responses about the usage of the system are acceptable and interesting. But this kind of test and users' suggestions for improvements need to be further explored. Keywords—mobile navigation, indoor navigation, scale-invariant feature transform (SIFT), image recognition