Performance Evaluation and Integration of Distortion Mitigation Methods for Fisheye Video Object Detection
The distortion observed in fisheye cameras has proven to be a persistent challenge for numerous state-of-the-art object detection algorithms, instigating the development of various techniques aimed at mitigating this issue. This study aims to evaluate various methods for mitigating distortion in fis...
Saved in:
Main Authors: | , , |
---|---|
Format: | text |
Published: |
Archīum Ateneo
2024
|
Subjects: | |
Online Access: | https://archium.ateneo.edu/ecce-faculty-pubs/161 https://archium.ateneo.edu/context/ecce-faculty-pubs/article/1155/viewcontent/21211_41688_1_PB.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Ateneo De Manila University |
id |
ph-ateneo-arc.ecce-faculty-pubs-1155 |
---|---|
record_format |
eprints |
spelling |
ph-ateneo-arc.ecce-faculty-pubs-11552024-09-23T03:16:25Z Performance Evaluation and Integration of Distortion Mitigation Methods for Fisheye Video Object Detection Du, John Benedict Mayuga, Gian Paolo Guico, Maria Leonora The distortion observed in fisheye cameras has proven to be a persistent challenge for numerous state-of-the-art object detection algorithms, instigating the development of various techniques aimed at mitigating this issue. This study aims to evaluate various methods for mitigating distortion in fisheye camera footage and their impact on video object detection accuracy and speed. Using Python, OpenCV, and third-party libraries, the researchers modified and optimized said methods for video input and created a framework for running and testing different distortion correction methods and object detection algorithm configurations. Through experimentation with different datasets, the study found that undistorting the image using the longitude-latitude correction with the YOLOv3 object detector provided the best results in terms of accuracy (PASCAL: 68.9%, VOC-360: 75.1%, WEPDTOF: 15.9%) and speed (38 FPS across all test sets) for fisheye footage. After measuring the results to determine the best configuration for video object detection, the researchers also developed a desktop application that incorporates these methods and provides real-time object detection and tracking functions. The study provides a foundation for improving the accuracy and speed of fisheye camera setups, and its findings can be valuable for researchers and practitioners working in this field. 2024-09-01T07:00:00Z text application/pdf https://archium.ateneo.edu/ecce-faculty-pubs/161 https://archium.ateneo.edu/context/ecce-faculty-pubs/article/1155/viewcontent/21211_41688_1_PB.pdf Electronics, Computer, and Communications Engineering Faculty Publications Archīum Ateneo Distortion mitigation Fisheye cameras Image remapping Longitudinal-latitude correction Real-time tracking Video object detection YOLOv3 Computer Engineering Electrical and Computer Engineering Electrical and Electronics Engineering |
institution |
Ateneo De Manila University |
building |
Ateneo De Manila University Library |
continent |
Asia |
country |
Philippines Philippines |
content_provider |
Ateneo De Manila University Library |
collection |
archium.Ateneo Institutional Repository |
topic |
Distortion mitigation Fisheye cameras Image remapping Longitudinal-latitude correction Real-time tracking Video object detection YOLOv3 Computer Engineering Electrical and Computer Engineering Electrical and Electronics Engineering |
spellingShingle |
Distortion mitigation Fisheye cameras Image remapping Longitudinal-latitude correction Real-time tracking Video object detection YOLOv3 Computer Engineering Electrical and Computer Engineering Electrical and Electronics Engineering Du, John Benedict Mayuga, Gian Paolo Guico, Maria Leonora Performance Evaluation and Integration of Distortion Mitigation Methods for Fisheye Video Object Detection |
description |
The distortion observed in fisheye cameras has proven to be a persistent challenge for numerous state-of-the-art object detection algorithms, instigating the development of various techniques aimed at mitigating this issue. This study aims to evaluate various methods for mitigating distortion in fisheye camera footage and their impact on video object detection accuracy and speed. Using Python, OpenCV, and third-party libraries, the researchers modified and optimized said methods for video input and created a framework for running and testing different distortion correction methods and object detection algorithm configurations. Through experimentation with different datasets, the study found that undistorting the image using the longitude-latitude correction with the YOLOv3 object detector provided the best results in terms of accuracy (PASCAL: 68.9%, VOC-360: 75.1%, WEPDTOF: 15.9%) and speed (38 FPS across all test sets) for fisheye footage. After measuring the results to determine the best configuration for video object detection, the researchers also developed a desktop application that incorporates these methods and provides real-time object detection and tracking functions. The study provides a foundation for improving the accuracy and speed of fisheye camera setups, and its findings can be valuable for researchers and practitioners working in this field. |
format |
text |
author |
Du, John Benedict Mayuga, Gian Paolo Guico, Maria Leonora |
author_facet |
Du, John Benedict Mayuga, Gian Paolo Guico, Maria Leonora |
author_sort |
Du, John Benedict |
title |
Performance Evaluation and Integration of Distortion Mitigation Methods for Fisheye Video Object Detection |
title_short |
Performance Evaluation and Integration of Distortion Mitigation Methods for Fisheye Video Object Detection |
title_full |
Performance Evaluation and Integration of Distortion Mitigation Methods for Fisheye Video Object Detection |
title_fullStr |
Performance Evaluation and Integration of Distortion Mitigation Methods for Fisheye Video Object Detection |
title_full_unstemmed |
Performance Evaluation and Integration of Distortion Mitigation Methods for Fisheye Video Object Detection |
title_sort |
performance evaluation and integration of distortion mitigation methods for fisheye video object detection |
publisher |
Archīum Ateneo |
publishDate |
2024 |
url |
https://archium.ateneo.edu/ecce-faculty-pubs/161 https://archium.ateneo.edu/context/ecce-faculty-pubs/article/1155/viewcontent/21211_41688_1_PB.pdf |
_version_ |
1811611615923011584 |