{"id":{"repo_id":"mississippi","oai_identifier":"oai:egrove.olemiss.edu:etd-2297"},"canonical_url":"https://search.dev.ndltd.org/etd/mississippi/oai:egrove.olemiss.edu:etd-2297","repository":{"repo_id":"mississippi","name":"University of Mississippi","base_url":"https://egrove.olemiss.edu/do/oai/"},"display":{"title":"A GPU Powered Mobile AR Navigation System","abstract":"This thesis presents a real-time Augmented Reality Navigation System(ARNavi) on Android smartphone that leverages the parallel computing power of mobile GPUs. Unlike conventional navigation systems, our proposed ARNavi augments navigation information onto real scene video streaming from device camera in real-time. To achieve this goal, we implement and accelerate compute intensive part of applications using OpenCL on GPU integrated on mobile Application Processor (AP). The contributions of this thesis are three-fold. First, we propose new lane detection algorithm and prediction mechanism based on geometric coordinates. The result shows that these two algorithms are fast and accurate. Second, we port and accelerate a complete application on mobile AP. By taking advantage of CPU-GPU heterogeneous computing techniques, we achieve more than 2.6 times performance boost compared to CPU only version. Lastly, we successfully integrate OpenCL and OpenCV on Android platform.","abstract_html":"This thesis presents a real-time Augmented Reality Navigation System(ARNavi) on Android smartphone that leverages the parallel computing power of mobile GPUs. Unlike conventional navigation systems, our proposed ARNavi augments navigation information onto real scene video streaming from device camera in real-time. To achieve this goal, we implement and accelerate compute intensive part of applications using OpenCL on GPU integrated on mobile Application Processor (AP). The contributions of this thesis are three-fold. First, we propose new lane detection algorithm and prediction mechanism based on geometric coordinates. The result shows that these two algorithms are fast and accurate. Second, we port and accelerate a complete application on mobile AP. By taking advantage of CPU-GPU heterogeneous computing techniques, we achieve more than 2.6 times performance boost compared to CPU only version. Lastly, we successfully integrate OpenCL and OpenCV on Android platform.","abstract_has_math":false,"creators":["Zhao, Mengshen"],"institution":null,"degree_name":"M.S. in Engineering Science","degree_level":"Thesis","degree_discipline":"Computer and Information Science","degree_department":null,"school":null,"contributors":["Byunghyun Jang","Yixin Chen","Dawn Wilkins"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-01-01T08:00:00Z","date_published":"2015-01-01T08:00:00Z","updated_at":"2026-07-24T03:06:44Z","subjects":["Augmented Reality","Image Processing","Mobile GPU","Navigation System","OpenCL","OpenCV4Android","Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://egrove.olemiss.edu/etd/1298","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Byunghyun Jang","Yixin Chen","Dawn Wilkins"]},{"key":"dc:creator","label":"Author","values":["Zhao, Mengshen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2020-01-23T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer and Information Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S. in Engineering Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Augmented Reality","Image Processing","Mobile GPU","Navigation System","OpenCL","OpenCV4Android","Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://egrove.olemiss.edu/etd/1298"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis presents a real-time Augmented Reality Navigation System(ARNavi) on Android smartphone that leverages the parallel computing power of mobile GPUs. Unlike conventional navigation systems, our proposed ARNavi augments navigation information onto real scene video streaming from device camera in real-time. To achieve this goal, we implement and accelerate compute intensive part of applications using OpenCL on GPU integrated on mobile Application Processor (AP). The contributions of this thesis are three-fold. First, we propose new lane detection algorithm and prediction mechanism based on geometric coordinates. The result shows that these two algorithms are fast and accurate. Second, we port and accelerate a complete application on mobile AP. By taking advantage of CPU-GPU heterogeneous computing techniques, we achieve more than 2.6 times performance boost compared to CPU only version. Lastly, we successfully integrate OpenCL and OpenCV on Android platform."]},{"key":"dc:title","label":"Title","values":["A GPU Powered Mobile AR Navigation System"]}]}],"canonical_facts":{"dc:contributor":["Byunghyun Jang","Yixin Chen","Dawn Wilkins"],"dc:creator":["Zhao, Mengshen"],"dc:date.available":["2020-01-23T08:00:00Z"],"dc:description.abstract":["This thesis presents a real-time Augmented Reality Navigation System(ARNavi) on Android smartphone that leverages the parallel computing power of mobile GPUs. Unlike conventional navigation systems, our proposed ARNavi augments navigation information onto real scene video streaming from device camera in real-time. To achieve this goal, we implement and accelerate compute intensive part of applications using OpenCL on GPU integrated on mobile Application Processor (AP). The contributions of this thesis are three-fold. First, we propose new lane detection algorithm and prediction mechanism based on geometric coordinates. The result shows that these two algorithms are fast and accurate. Second, we port and accelerate a complete application on mobile AP. By taking advantage of CPU-GPU heterogeneous computing techniques, we achieve more than 2.6 times performance boost compared to CPU only version. Lastly, we successfully integrate OpenCL and OpenCV on Android platform."],"dc:identifier":["https://egrove.olemiss.edu/etd/1298"],"dc:subject":["Augmented Reality","Image Processing","Mobile GPU","Navigation System","OpenCL","OpenCV4Android","Engineering"],"dc:title":["A GPU Powered Mobile AR Navigation System"],"thesis:degree_discipline":["Computer and Information Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S. in Engineering Science"]},"updated_at":"2026-07-24T03:06:44Z"}