2026-09-20T02:13:51Zhttps://ruor.uottawa.ca/server/oai/request

oai:ruor.uottawa.ca:10393/243042024-02-23T09:01:03Zcom_10393_242col_10393_11105

People Tracking Under Occlusion Using Gaussian Mixture Model and Fast Level Set Energy Minimization Moradiannejad, Ghazaleh Laganiere, Robert People Tracking occlusion 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. 2013-07-09T16:13:15Z 2013-07-09T16:13:15Z 2013 2013 Thesis http://hdl.handle.net/10393/24304 http://dx.doi.org/10.20381/ruor-3089 en application/pdf Université d'Ottawa / University of Ottawa