Monocular 3D Object Detection And Tracking in Industrial Settings

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http://urn.fi/URN:NBN:fi:hulib-202105112140
Title: Monocular 3D Object Detection And Tracking in Industrial Settings
Author: Leinonen, Matti
Contributor: University of Helsinki, Faculty of Science
Publisher: Helsingin yliopisto
Date: 2021
Language: eng
URI: http://urn.fi/URN:NBN:fi:hulib-202105112140
http://hdl.handle.net/10138/329797
Thesis level: master's thesis
Degree program: Tietojenkäsittelytieteen maisteriohjelma
Master's Programme in Computer Science
Magisterprogrammet i datavetenskap
Specialisation: Algoritmit
Algorithms
Algoritmer
Abstract: 3D Object detection and tracking are computer vision methods used in many applications. It is necessary for autonomous vehicles and robots to be able to reliably extract 3D localization information about objects in their environment to operate safely. Currently most 3D object detection and tracking algorithms use high quality LiDAR-sensors which are very expensive. This is why research into methods that use cheap monocular camera images as inputs is an active field in computer vision research. Most current research into monocular 3D object detection and tracking is focused in autonomous driving. This thesis investigates how well current monocular methods are suited for use in industrial settings where the environment and especially the camera perspective can be very different compared to what it is in an automobile. This thesis introduces some of the most used 3D object detection and tracking methods and techniques and tests one detection method on a dataset where the environment and the point of view differs from what it would be in autonomous driving. This thesis also analyzes the technical requirements for a detection and tracking system that could be be used for autonomous robots in an industrial setting and what future research would be necessary to develop such a system.
Subject: computer vision
3d object detection
3d object tracking
industrial robots
autonomous cranes


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