City Street View Image Localization and Study on Multiple Descriptors Matching

• Main Authors: Zhi-An Chen, 陳治安
• Other Authors: Jen-Chang Liu
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/4prkru

碩士 === 國立暨南國際大學 === 資訊工程學系 === 105 === Panoramic image is difference from traditional image because it can record rich space information. More applications of panorama appear in our daily life, such as Google street view, panoramic recording using Unmanned Aerial Vehicle, virtual reality with panoramic guide. Since Google started the service of street view, users can search attractions from Google street view by the Panoramic images which recorded from this attraction, and they can know rich space information from this attraction, such as information of stores, roads, traffic flow, etc. Google street view is praised from people after it was released, so recording street view has great value. We continue the work [1] for collecting street view data in a city by the car mounted with Ladybug3 [2], and regularly recording the panorama of Puli street view and GPS locations. It will be the data source for image-based localization. Started from the work [1] until now, we recorded 22 times in 2 years, and the original video stream data is 2.63 TB, which transforming to 694,301 pieces of the equirectangular images in 8000 x 4000 resolution and stored in NAS server for users to query. Two applications were studied in this thesis. The first application is about image-based localization using the iconic images, such as the flags. We manually cut a set of iconic images from the recorded panoramic images, and used the SIFT descriptor to extract features for image search. The second study is about the method of Multiple Descriptors Fusion [3], which fuses many descriptors of different properties. An experiment was conducted for image-based localization using multiple descriptors, and the other experiment was to compare the performance using multiple descriptors and a single descriptor.