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Partitioned particle filtering for target tracking in video sequences
[page 9-12,17,18 are missing] A partitioned particle filtering algorithm is developed to track moving targets exhibiting complex interaction in a static environment, in a video sequence. The filter is augmented with an additional scan phase, which is a deterministic sequence which has been formulate...
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| Format: | Thesis |
| Language: | English |
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Department of Electrical Engineering
2024
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| _version_ | 1867614003875807232 |
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| access_status_str | Open Access |
| author | Louw, Markus Smuts |
| author2 | de Jager, G |
| author_browse | Louw, Markus Smuts de Jager, G |
| author_facet | de Jager, G Louw, Markus Smuts |
| author_sort | Louw, Markus Smuts |
| collection | Thesis |
| description | [page 9-12,17,18 are missing] A partitioned particle filtering algorithm is developed to track moving targets exhibiting complex interaction in a static environment, in a video sequence. The filter is augmented with an additional scan phase, which is a deterministic sequence which has been formulated in terms of the recursive Bayesian paradigm, and yields superior results. One partition is allocated to each target object, and a joint hypothesis is made for simultaneous location of all targets in world coordinates. The observation likelihood is calculated on a per-pixel basis, using sixteen-centered Gaussian Mixture Models trained on the available colour information for each target. Assumptions about the behaviour of each pixel allow for the improvement under certain circumstances of the basic pixel classification by smoothing, using Hidden Markov Models, again on a per-pixel basis. The tracking algorithm produces very good results, both on a complex sequence using highly identifiable targets, as well as on a simpler sequence with natural targets. In each of the scenes, all of the targets were correctly tracked for a very high percentage of the frames in which they were present, and each target loss was followed by a successful reacquisition. Two hundred basic particles were used per partition, with an additional one hundred augmented particles per partition, for the scan phase. The algorithm does not run in real-time, although with optimization this is a possibility. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/40220 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:45:08.457Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Department of Electrical Engineering |
| publisherStr | Department of Electrical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/40220 Partitioned particle filtering for target tracking in video sequences Louw, Markus Smuts de Jager, G Nicolls, Frederick Electrical Engineering [page 9-12,17,18 are missing] A partitioned particle filtering algorithm is developed to track moving targets exhibiting complex interaction in a static environment, in a video sequence. The filter is augmented with an additional scan phase, which is a deterministic sequence which has been formulated in terms of the recursive Bayesian paradigm, and yields superior results. One partition is allocated to each target object, and a joint hypothesis is made for simultaneous location of all targets in world coordinates. The observation likelihood is calculated on a per-pixel basis, using sixteen-centered Gaussian Mixture Models trained on the available colour information for each target. Assumptions about the behaviour of each pixel allow for the improvement under certain circumstances of the basic pixel classification by smoothing, using Hidden Markov Models, again on a per-pixel basis. The tracking algorithm produces very good results, both on a complex sequence using highly identifiable targets, as well as on a simpler sequence with natural targets. In each of the scenes, all of the targets were correctly tracked for a very high percentage of the frames in which they were present, and each target loss was followed by a successful reacquisition. Two hundred basic particles were used per partition, with an additional one hundred augmented particles per partition, for the scan phase. The algorithm does not run in real-time, although with optimization this is a possibility. 2024-07-02T10:22:49Z 2024-07-02T10:22:49Z 2004 2024-06-25T13:53:28Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/40220 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment |
| spellingShingle | Electrical Engineering Louw, Markus Smuts Partitioned particle filtering for target tracking in video sequences |
| thesis_degree_str | Master's |
| title | Partitioned particle filtering for target tracking in video sequences |
| title_full | Partitioned particle filtering for target tracking in video sequences |
| title_fullStr | Partitioned particle filtering for target tracking in video sequences |
| title_full_unstemmed | Partitioned particle filtering for target tracking in video sequences |
| title_short | Partitioned particle filtering for target tracking in video sequences |
| title_sort | partitioned particle filtering for target tracking in video sequences |
| topic | Electrical Engineering |
| url | http://hdl.handle.net/11427/40220 |
| work_keys_str_mv | AT louwmarkussmuts partitionedparticlefilteringfortargettrackinginvideosequences |