{"id":{"repo_id":"ottawa-retro","oai_identifier":"oai:ruor.uottawa.ca:10393/24304"},"canonical_url":"https://search.dev.ndltd.org/etd/ottawa-retro/oai:ruor.uottawa.ca:10393/24304","repository":{"repo_id":"ottawa-retro","name":"University of Ottawa","base_url":"https://ruor.uottawa.ca/server/oai/request"},"display":{"title":"People Tracking Under Occlusion Using Gaussian Mixture Model and Fast Level Set Energy Minimization","abstract":"Tracking multiple articulated objects (such as a human body) and handling occlusion between them is a challenging problem in automated video analysis. This work proposes a new approach for accurately and steadily visual tracking people, which should function even if the system encounters occlusion in video sequences. In this approach, targets are represented with a Gaussian mixture, which are adapted to regions of the target automatically using an EM-model algorithm. Field speeds are defined for changed pixels in each frame based on the probability of their belonging to a particular person's blobs. Pixels are matched to the models using a fast numerical level set method. Since each target is tracked with its blob's information, the system is capable of handling partial or full occlusion during tracking. Experimental results on a number of challenging sequences that were collected in non-experimental environments demonstrate the effectiveness of the approach.","abstract_html":"Tracking multiple articulated objects (such as a human body) and handling occlusion between them is a challenging problem in automated video analysis. This work proposes a new approach for accurately and steadily visual tracking people, which should function even if the system encounters occlusion in video sequences. In this approach, targets are represented with a Gaussian mixture, which are adapted to regions of the target automatically using an EM-model algorithm. Field speeds are defined for changed pixels in each frame based on the probability of their belonging to a particular person's blobs. Pixels are matched to the models using a fast numerical level set method. Since each target is tracked with its blob's information, the system is capable of handling partial or full occlusion during tracking. 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This work proposes a new approach for accurately and steadily visual tracking people, which should function even if the system encounters occlusion in video sequences. In this approach, targets are represented with a Gaussian mixture, which are adapted to regions of the target automatically using an EM-model algorithm. Field speeds are defined for changed pixels in each frame based on the probability of their belonging to a particular person's blobs. Pixels are matched to the models using a fast numerical level set method. Since each target is tracked with its blob's information, the system is capable of handling partial or full occlusion during tracking. Experimental results on a number of challenging sequences that were collected in non-experimental environments demonstrate the effectiveness of the approach."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["People Tracking Under Occlusion Using Gaussian Mixture Model and Fast Level Set Energy Minimization"]}]}],"canonical_facts":{"dc:contributor":["Laganiere, Robert"],"dc:creator":["Moradiannejad, Ghazaleh"],"dc:date":["2013-07-09T16:13:15Z","2013"],"dc:description":["Tracking multiple articulated objects (such as a human body) and handling occlusion between them is a challenging problem in automated video analysis. This work proposes a new approach for accurately and steadily visual tracking people, which should function even if the system encounters occlusion in video sequences. In this approach, targets are represented with a Gaussian mixture, which are adapted to regions of the target automatically using an EM-model algorithm. Field speeds are defined for changed pixels in each frame based on the probability of their belonging to a particular person's blobs. Pixels are matched to the models using a fast numerical level set method. Since each target is tracked with its blob's information, the system is capable of handling partial or full occlusion during tracking. Experimental results on a number of challenging sequences that were collected in non-experimental environments demonstrate the effectiveness of the approach."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10393/24304","http://dx.doi.org/10.20381/ruor-3089"],"dc:language":["en"],"dc:publisher":["Université d'Ottawa / University of Ottawa"],"dc:subject":["People Tracking","occlusion"],"dc:title":["People Tracking Under Occlusion Using Gaussian Mixture Model and Fast Level Set Energy Minimization"],"dc:type":["Thesis"]},"updated_at":"2026-07-24T03:39:25Z"}