Object Detection in Omnidirectional Images

Henriques, Francisco António Agostinho

Publication

Object Detection in Omnidirectional Images

2021-01-26Master thesis

datacite.subject.fos	Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática	pt_PT
dc.contributor.advisor	Costa, Joana Madeira Martins
dc.contributor.advisor	Assunção, Pedro António Amado de
dc.contributor.advisor	Silva, Catarina Helena Branco Simões da
dc.contributor.author	Henriques, Francisco António Agostinho
dc.date.accessioned	2021-03-31T09:40:50Z
dc.date.available	2021-03-31T09:40:50Z
dc.date.issued	2021-01-26
dc.description.abstract	Nowadays, computer vision (CV) is widely used to solve real-world problems, which pose increasingly higher challenges. In this context, the use of omnidirectional video in a growing number of applications, along with the fast development of Deep Learning (DL) algorithms for object detection, drives the need for further research to improve existing methods originally developed for conventional 2D planar images. However, the geometric distortion that common sphere-to-plane projections produce, mostly visible in objects near the poles, in addition to the lack of omnidirectional open-source labeled image datasets has made an accurate spherical image-based object detection algorithm a hard goal to achieve. This work is a contribution to develop datasets and machine learning models particularly suited for omnidirectional images, represented in planar format through the well-known Equirectangular Projection (ERP). To this aim, DL methods are explored to improve the detection of visual objects in omnidirectional images, by considering the inherent distortions of ERP. An experimental study was, firstly, carried out to find out whether the error rate and type of detection errors were related to the characteristics of ERP images. Such study revealed that the error rate of object detection using existing DL models with ERP images, actually, depends on the object spherical location in the image. Then, based on such findings, a new object detection framework is proposed to obtain a uniform error rate across the whole spherical image regions. The results show that the pre and post-processing stages of the implemented framework effectively contribute to reducing the performance dependency on the image region, evaluated by the above-mentioned metric.	pt_PT
dc.identifier.tid	202689514	pt_PT
dc.identifier.uri	http://hdl.handle.net/10400.8/5591
dc.language.iso	eng	pt_PT
dc.subject	Computer Vision	pt_PT
dc.subject	Deep Learning	pt_PT
dc.subject	Object Detection	pt_PT
dc.subject	Equirectangular Projection	pt_PT
dc.subject	Omnidirectional images	pt_PT
dc.title	Object Detection in Omnidirectional Images	pt_PT
dc.type	master thesis
dspace.entity.type	Publication
rcaap.rights	openAccess	pt_PT
rcaap.type	masterThesis	pt_PT
thesis.degree.name	Mestrado em Engenharia Informática - Computação Móvel	pt_PT

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