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EnglishIn this paper we propose a novel framework for the detection and tracking in real-time of unknown object in a video stream. We decompose the problem into two separate modules: detection and learning. The detection module can use multiple keypoint-based methods (ORB, FREAK, BRISK, SIFT, SURF and more) inside a fallback model, to correctly localize the object frame by frame exploiting the strengths of each method. The learning module updates the object model, with a growing and pruning approach, to account for changes in its appearance and extracts negative samples to further improve the detector performance. To show the effectiveness of the proposed tracking-by-detection algorithm, we present quantitative results on a number of challenging sequences where the target object goes through changes of pose, scale and illumination.
| Titolo: | MATRIOSKA: A Multi-level Approach to Fast Tracking by Learning | |
| Autori: | ||
| Data di pubblicazione: | 2013 | |
| Rivista: | ||
| Abstract: | In this paper we propose a novel framework for the detection and tracking in real-time of unknown object in a video stream. We decompose the problem into two separate modules: detection and learning. The detection module can use multiple keypoint-based methods (ORB, FREAK, BRISK, SIFT, SURF and more) inside a fallback model, to correctly localize the object frame by frame exploiting the strengths of each method. The learning module updates the object model, with a growing and pruning approach, to account for changes in its appearance and extracts negative samples to further improve the detector performance. To show the effectiveness of the proposed tracking-by-detection algorithm, we present quantitative results on a number of challenging sequences where the target object goes through changes of pose, scale and illumination. | |
| Handle: | http://hdl.handle.net/11367/32315 | |
| ISBN: | 978-3-642-41183-0 978-3-642-41184-7 978-3-642-41183-0 978-3-642-41184-7 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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