Partitioned particle filtering for target tracking in video sequences
| dc.contributor.advisor | de Jager, G | |
| dc.contributor.advisor | Nicolls, Frederick | |
| dc.contributor.author | Louw, Markus Smuts | |
| dc.date.accessioned | 2024-07-02T10:22:49Z | |
| dc.date.available | 2024-07-02T10:22:49Z | |
| dc.date.issued | 2004 | |
| dc.date.updated | 2024-06-25T13:53:28Z | |
| dc.description.abstract | [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. | |
| dc.identifier.apacitation | Louw, M. S. (2004). <i>Partitioned particle filtering for target tracking in video sequences</i>. (). ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/40220 | en_ZA |
| dc.identifier.chicagocitation | Louw, Markus Smuts. <i>"Partitioned particle filtering for target tracking in video sequences."</i> ., ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering, 2004. http://hdl.handle.net/11427/40220 | en_ZA |
| dc.identifier.citation | Louw, M.S. 2004. Partitioned particle filtering for target tracking in video sequences. . ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering. http://hdl.handle.net/11427/40220 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Louw, Markus Smuts AB - [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. DA - 2004 DB - OpenUCT DP - University of Cape Town KW - Electrical Engineering LK - https://open.uct.ac.za PY - 2004 T1 - Partitioned particle filtering for target tracking in video sequences TI - Partitioned particle filtering for target tracking in video sequences UR - http://hdl.handle.net/11427/40220 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/40220 | |
| dc.identifier.vancouvercitation | Louw MS. Partitioned particle filtering for target tracking in video sequences. []. ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering, 2004 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/40220 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Electrical Engineering | |
| dc.publisher.faculty | Faculty of Engineering and the Built Environment | |
| dc.subject | Electrical Engineering | |
| dc.title | Partitioned particle filtering for target tracking in video sequences | |
| dc.type | Thesis / Dissertation | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |