Date of Publication
7-6-2022
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Computer Science Major in Computer Systems Engineering
Subject Categories
Computer Sciences
College
College of Computer Studies
Department/Unit
Computer Technology
Thesis Advisor
Macario O. Cordel, II
Defense Panel Chair
Joel P. Ilao
Defense Panel Member
Clement Y. Ong
Roger Luis Uy
Abstract/Summary
Numerous works on detecting and geotagging street objects utilize feeds from fixed traffic cameras However, these systems are limited to detecting objects passing through the traffic cameras’ field of view, and cannot be used to provide a complete picture of objects around an area. In this work, we used a smartphone dashboard camera with a built-in GPS and magnetometer sensors to acquire street view footage allowing the system to perform simultaneous detection and geotagging of target objects. Deep learning approach for object detection and depth estimation for geotagging are used as core algorithms of the system. As opposed to fixed traffic cameras, dashboard cameras allow the crowd participation in data collection. The developed system has 81.12% mAP in object detection and 1.53 m average MAE in geotagging. A visualization application was developed to showcase the potential of the system
Abstract Format
html
Language
English
Format
Electronic
Physical Description
[140 leaves]
Keywords
Traffic cameras; Global Positioning System
Recommended Citation
Infante, R. H., Mendoza, J. M., Ambrosio, K. M., & Fernandez, J. T. (2022). Geotagging and object detection using dashcam (GEODASH). Retrieved from https://animorepository.dlsu.edu.ph/etdb_comtech/2
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Embargo Period
7-6-2022
