Date of Publication

2021

Document Type

Master's Thesis

Degree Name

Master of Science in Electronics and Communications Engineering

Subject Categories

Electrical and Computer Engineering

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Thesis Advisor

Jay Robert B. del Rosario

Defense Panel Chair

Argel A. Bandala

Defense Panel Member

Robert Kerwin D. Billiones
Jose Martin Z. Maningo

Abstract/Summary

Image and video data annotation is an effort and time-consuming process. However, it is necessary for training neural network models for computer vision applications such as in Intelligent Transport Systems. Thus, this research explores ways in order to reduce the time and effort necessary to establish ground truth annotations for videos and images specifically for ITS applications. The approach of this research is by using existing computer vision and machine learning tools and methodologies to aid the annotation process. Mainly, the research explored the use of trained models to create the initial bounding boxes in the video or image being annotated. The generated bounding boxes are then tracked using Siamese-based trackers with the aid of a human annotator which modifies the result as necessary.

Abstract Format

html

Language

English

Physical Description

154 leaves, color illustrations

Keywords

Intelligent transportation systems; Computer vision; Image processing—Digital techniques

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Embargo Period

6-14-2021

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