Computational geometric-based visual shape tracker
| dc.contributor.author | Bandara, Pasan | |
| dc.contributor.examiningcommittee | Major, Arkady (Electrical and Computer Engineering) Sherif, Sherif (Electrical and Computer Engineering) Hyun Ko, Ji (Human Anatomy and Cell Science) | en_US |
| dc.contributor.supervisor | Peters, James (Electrical and Computer Engineering) | en_US |
| dc.date.accessioned | 2021-08-04T20:48:13Z | |
| dc.date.available | 2021-08-04T20:48:13Z | |
| dc.date.copyright | 2021-07-22 | |
| dc.date.issued | 2021-07 | en_US |
| dc.date.submitted | 2021-06-13T18:00:07Z | en_US |
| dc.date.submitted | 2021-07-22T20:12:17Z | en_US |
| dc.degree.discipline | Electrical and Computer Engineering | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | The main intent of multiple shape tracking is to allocate distinct track identities for all the salient shapes in a video sequence. The approach used in this problem is ‘Track by Detection’ scheme, where it needs to first, find shapes in a frame, map shapes to appropriate tracking tracks, and maintain the data continuity model throughout the video sequence. This thesis proposes a process to address shape tracking by maintaining a data continuity model based on shape relative proximity, shape context descriptor, and spatial color variation of a shape. Shape intersection is used to calculate the shape proximity. A spatial color analyzer is used to compare shape’s color variations between two consecutive frames, and shape similarity comparison is done by using the shape context descriptor. These values are then used to build and maintain the data continuity model throughout the video sequence. Besides, this also introduces a methodology to reestablish tracking identity when the detection is failed or occluded temporarily between small numbers of frames. The proposed method can be implemented using both online and offline approaches. To assess the proposed method’s robustness, we evaluated the online method in both VOT-ST2020 (Visual Object Tracking Challenge - Short Term) and MOT20 (Multiple Object Tracking) benchmark datasets. It was observed that in both benchmarks, the proposed approach can deliver state-of-the-art results. | en_US |
| dc.description.note | October 2021 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/35782 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Computer vision | en_US |
| dc.subject | Shape tracking | en_US |
| dc.title | Computational geometric-based visual shape tracker | en_US |
| dc.type | master thesis | en_US |