In this thesis, a novel Object Tracking Algorithm is proposed which tracks objects on Apple iPhone 4 platform in real-time. The system utilizes the colorspace of the frames provided by iPhone camera, in parallel with the motion data provided by iPhone motion sensors, to cancel the effect of iPhone rotations during tracking and matching different candidate tracks. The proposed system also adapts to changes in target appearance and size, thus leading to an object tracking robust to such changes. Several experiments conducted on actual video sequences are used to illustrate the functionality of the proposed approach.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/30625 |
| Date | 08 December 2011 |
| Creators | Heidari, Amin |
| Contributors | Aarabi, Parham |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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