Transformer-Based Multi-Object Tracking of Football Players Using Pseudo-Labeling
dc.contributor.author | Hedén, Anton | |
dc.contributor.author | Odengard, Anthon | |
dc.contributor.department | Chalmers tekniska högskola / Institutionen för elektroteknik | sv |
dc.contributor.examiner | McKelvey, Tomas | |
dc.contributor.supervisor | Sjöberg, Anders | |
dc.contributor.supervisor | Svensson, Lennart | |
dc.date.accessioned | 2025-06-30T11:38:19Z | |
dc.date.issued | 2025 | |
dc.date.submitted | ||
dc.description.abstract | This thesis investigates the problem of tracking football players in video sequences, with a focus on adapting modern multi-object tracking (MOT) methods to the specific challenges of football environments. The work is part of a broader effort to develop tools for analyzing football games using automated visual data. In this context, we utilize MOTRv2, a transformer-based tracking model originally designed for general-purpose MOT tasks, and apply it to the football domain, where challenges such as frequent occlusions, tight formations, and rapid movement are prevalent. To address the lack of annotated football-specific tracking data, we implement a pseudo-labeling framework that allows the model to be trained on unlabelled domain data in a semi-supervised fashion. This approach enables progressive refinement of the model through multiple training cycles on domain-specific content. Our results show that MOTRv2 can be adapted to the football setting and performs well in many scenarios, particularly in open-play segments with clear player separation. However, limitations remain, including decreased tracking stability in crowded scenes and occasional ID-switches due to overlapping motion patterns. Overall, this work demonstrates the potential of transformer-based trackers in sports applications and highlights the benefits of self-supervised training when domainspecific data is scarce. The findings offer insights for future improvements in automated sports tracking systems. | |
dc.identifier.coursecode | EENX30 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12380/309777 | |
dc.language.iso | eng | |
dc.setspec.uppsok | Technology | |
dc.subject | Computer Vision | |
dc.subject | Multiple Object Tracking | |
dc.subject | Transformer | |
dc.subject | Football | |
dc.subject | Pseudo-Labeling | |
dc.subject | Re-ID | |
dc.title | Transformer-Based Multi-Object Tracking of Football Players Using Pseudo-Labeling | |
dc.type.degree | Examensarbete för masterexamen | sv |
dc.type.degree | Master's Thesis | en |
dc.type.uppsok | H | |
local.programme | Complex adaptive systems (MPCAS), MSc |
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