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Arakawa Kaoru
Department Undergraduate School , School of Interdisciplinary Mathematical Sciences Position Professor |
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| Language | English |
| Publication Date | 2018/11 |
| Type | International Conference |
| Peer Review | Peer reviewed |
| Title | Kernel Correlation Filter Tracker via Adaptive Model |
| Contribution Type | Co-authored (other than first author) |
| Journal | Proc. ISPACS 2018 |
| Journal Type | Another Country |
| Publisher | IEEE |
| Volume, Issue, Page | pp.205-209 |
| Total page number | 5 |
| Author and coauthor | Tang Zhaoqian, Kaoru Arakawa |
| Details | In this paper, we propose a robust object tracking algorithm using an adaptive model and robust state recognition in kernel
correlation filter tracker. In the first stage of the proposed algorithm, we apply a scale pool technique to deal with scale variation in object tracking. Then, the detection response from kernel correlation filter tracker is combined with grayscale histogram similarity to estimate the state of the object. Furthermore, the classifier model is updated with adjustable learning rate, thereby enabling the tracker to be robust to the change of the state of the object. Experimental results demonstrate that the proposed tracker realizes outstanding performance on a challenging benchmark (OTB). |
| ISBN | 978-1-5386-5770-6 |