Multiple Object TrackingCui, Aiyu Computer VisionTrackingMotion CaptureTaijiComputer ScienceB SAn important task in computer vision is tracking objects precisely to enable further data analysis. However, in reality, we can not guarantee that the tracking results out of a motion capture (MoCap) system are always accurate, due to lack of cameras numbers, limitation of camera coverage and so on. Unfortunately, manually fixing these errors can take more than 10 hours for a five-minute MoCap. Therefore, we are motivated to develop a method to fix imperfect motion capture data in terms of tracking correctness and gap filling. This thesis presents an innovative tracking algorithm to track objects formed by multiple trajectories with geometric correlations. In this thesis, we use a human skeleton formed by 39 markers (i.e. 39 trajectories) to illustrate how our algorithm can apply geometric constraints and select results among multiple results. We extend our algorithm to track multiple subjects (e.g. dual players and a human with a sword).Dr. Robert Collins, Thesis SupervisorDanfeng Zhang, Thesis Honors Advisoropen_access2018-04-10T11:35:35Zhttps://honors.libraries.psu.edu/catalog/5077abc5462