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Bayesian online homography estimation and its use in multiple object tracking
Thesis (PhD (Computer Engineering))--University of Pretoria, 2025.
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| Format: | Thesis |
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University of Pretoria
2026
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| _version_ | 1867613660915957760 |
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| access_status_str | Open Access |
| author2 | De Villiers, Johan Pieter |
| author_browse | De Villiers, Johan Pieter |
| author_facet | De Villiers, Johan Pieter |
| collection | Thesis |
| dc_rights_str_mv | © 2024 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Thesis (PhD (Computer Engineering))--University of Pretoria, 2025. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/107846 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:39:41.079Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/107846 Bayesian online homography estimation and its use in multiple object tracking De Villiers, Johan Pieter pj@benjamin.ng.org.za Claasen, Paul Johannes UCTD Sustainable Development Goals (SDGs) Thesis (PhD (Computer Engineering))--University of Pretoria, 2025. A novel Bayesian homography estimation framework is proposed, which explicitly relates the homography of one video frame to the next through an affine transformation while explicitly modelling keypoint uncertainty. The literature has previously used differential homography between subsequent frames, but not in a Bayesian setting. In cases where Bayesian methods have been applied, camera motion is not adequately modelled, and keypoints are treated as deterministic. The proposed method, Bayesian Homography Inference from Tracked Keypoints (BHITK), employs a two-stage Kalman filter and significantly improves existing methods. Existing keypoint detection methods may be easily augmented with BHITK. It enables less sophisticated and less computationally expensive methods to outperform the state-of-the-art approaches in most homography evaluation metrics. Furthermore, the homography annotations of the WorldCup and TS-WorldCup datasets have been refined using a custom homography annotation tool that has been released for public use. The refined datasets are consolidated and released as the consolidated and refined WorldCup (CARWC) dataset. In addition, a novel multiple object tracking (MOT) algorithm, IMM Joint Homography State Estimation (IMM-JHSE), is proposed. IMM-JHSE uses an initial homography estimate as the only additional 3D information, whereas other 3D MOT methods use regular 3D measurements. By jointly modelling the homography matrix and its dynamics as part of track state vectors, IMM-JHSE removes the explicit influence of camera motion compensation techniques on predicted track position states, which was prevalent in previous approaches. Expanding upon this, static and dynamic camera motion models are combined using an interacting multiple model (IMM) filter. A simple bounding box motion model is used to predict bounding box positions to incorporate image plane information. In addition to applying an IMM to camera motion, a non-standard IMM approach is applied where bounding-box-based BIoU scores are mixed with ground-plane-based Mahalanobis distances in an IMM-like fashion to perform association only, making IMM-JHSE robust to motion away from the ground plane. Finally, IMM-JHSE makes use of dynamic process and measurement noise estimation techniques. IMM-JHSE improves upon related techniques, including UCMCTrack, OC-SORT, C-BIoU and ByteTrack on the DanceTrack and KITTI-car datasets, increasing HOTA by 2.64 and 2.11, respectively, while offering competitive performance on the MOT17, MOT20 and KITTI-pedestrian datasets. Using publicly available detections, IMM-JHSE outperforms almost all other 2D MOT methods and is outperformed only by 3D MOT methods---some of which are offline---on the KITTI-car dataset. Compared to tracking-by-attention methods, IMM-JHSE shows remarkably similar performance on the DanceTrack dataset, achieving 66.24 HOTA. In comparison, a variant of MeMOTR achieves 66.70 HOTA. IMM-JHSE outperforms tracking-by-attention methods on the MOT17 dataset, achieving 64.90 HOTA, where MOTIP achieves 59.2 HOTA. MultiChoice Chair in Machine Learning Electrical, Electronic and Computer Engineering PhD (Computer Engineering) Unrestricted Faculty of Engineering, Built Environment and Information Technology None 2026-02-04T13:25:44Z 2026-02-04T13:25:44Z 2026-05-04 2025-06-30 Thesis * A2026 http://hdl.handle.net/2263/107846 https://doi.org/10.25403/UPresearchdata.31247296 en © 2024 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Sustainable Development Goals (SDGs) Bayesian online homography estimation and its use in multiple object tracking |
| title | Bayesian online homography estimation and its use in multiple object tracking |
| title_full | Bayesian online homography estimation and its use in multiple object tracking |
| title_fullStr | Bayesian online homography estimation and its use in multiple object tracking |
| title_full_unstemmed | Bayesian online homography estimation and its use in multiple object tracking |
| title_short | Bayesian online homography estimation and its use in multiple object tracking |
| title_sort | bayesian online homography estimation and its use in multiple object tracking |
| topic | UCTD Sustainable Development Goals (SDGs) |
| url | http://hdl.handle.net/2263/107846 https://doi.org/10.25403/UPresearchdata.31247296 |