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Extending Simple Online and Realtime Tracking with Sparse Optical Flow

Becker, Falko (2021) Extending Simple Online and Realtime Tracking with Sparse Optical Flow. Bachelor's, Humboldt-Universität zu Berlin.

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Detection and tracking of objects in video data is a common topic in computer vision and widely used for traffic surveillance. Simple Online and Realtime Tracking (SORT) is a Multi-Object Tracking (MOT) algorithm which performs in realtime by using efficient algorithms. SORT’s tracking performance is based on detection quality and may suffer due to detection failures. This work proposes an extension to SORT’s Kalman filter measurement model with Robust Local Optical Flow to add a velocity component to the filter’s update vector and improve robustness against detection failures and decrease track id switches. The elaborated extension, termed SORT_OF, is tested and benchmarked on parts of the MOT15 tracking benchmark as well as on a self created benchmark representing a DLR real world scenario. The proposed tracking method still works in realtime.

Item URL in elib:https://elib.dlr.de/141326/
Document Type:Thesis (Bachelor's)
Title:Extending Simple Online and Realtime Tracking with Sparse Optical Flow
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Date:17 February 2021
Refereed publication:No
Open Access:Yes
Number of Pages:42
Keywords:Multiple Object Tracking, Optical Flow, Kalman Filter, SORT, Computer Vision
Institution:Humboldt-Universität zu Berlin
Department:Institut für Informatik
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Transport System
DLR - Research area:Transport
DLR - Program:V VS - Verkehrssystem
DLR - Research theme (Project):V - Zukunftsbilder eines automatisierten integrierten Verkehrssystems (old)
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Transportation Systems > Information Gathering and Modelling, BA
Deposited By: Becker, Falko
Deposited On:18 Mar 2021 10:11
Last Modified:18 Mar 2021 10:11

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