Robust object tracking via online discriminative appearance modeling
Abstract A robust object tracking algorithm is proposed in this paper based on an online discriminative appearance modeling mechanism. In contrast with traditional trackers whose computations cover the whole target region and may easily be polluted by the similar background pixels, we divided the ta...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2019-10-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13634-019-0646-0 |
| _version_ | 1819206310444400640 |
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| author | Wei Liu Xin Sun Dong Li |
| author_facet | Wei Liu Xin Sun Dong Li |
| author_sort | Wei Liu |
| collection | DOAJ |
| description | Abstract A robust object tracking algorithm is proposed in this paper based on an online discriminative appearance modeling mechanism. In contrast with traditional trackers whose computations cover the whole target region and may easily be polluted by the similar background pixels, we divided the target into a number of patches and take the most discriminative one as the tracking basis. With the consideration of both the photometric and spatial information, we construct a discriminative target model on it. Then, a likelihood map can be got by comparing the target model with candidate regions, on which the mean shift procedure is employed for mode seeking. Finally, we update the target model to adapt to the appearance variation. Experimental results on a number of challenging video sequences confirm that the proposed method outperforms the related state-of-the-art trackers. |
| first_indexed | 2024-12-23T05:05:34Z |
| format | Article |
| id | doaj.art-9012a72b44cb489f9c7dd7713ac2f262 |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-12-23T05:05:34Z |
| publishDate | 2019-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-9012a72b44cb489f9c7dd7713ac2f2622022-12-21T17:59:05ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802019-10-01201911910.1186/s13634-019-0646-0Robust object tracking via online discriminative appearance modelingWei Liu0Xin Sun1Dong Li2Department of Modern Education Technology, Ludong UniversitySchool of Computer Science and Technology, Harbin Institute of TechnologySchool of Electrical and Information Engineering, Shandong UniveristyAbstract A robust object tracking algorithm is proposed in this paper based on an online discriminative appearance modeling mechanism. In contrast with traditional trackers whose computations cover the whole target region and may easily be polluted by the similar background pixels, we divided the target into a number of patches and take the most discriminative one as the tracking basis. With the consideration of both the photometric and spatial information, we construct a discriminative target model on it. Then, a likelihood map can be got by comparing the target model with candidate regions, on which the mean shift procedure is employed for mode seeking. Finally, we update the target model to adapt to the appearance variation. Experimental results on a number of challenging video sequences confirm that the proposed method outperforms the related state-of-the-art trackers.http://link.springer.com/article/10.1186/s13634-019-0646-0Visual trackingMean shiftOnline learningDiscriminative appearance |
| spellingShingle | Wei Liu Xin Sun Dong Li Robust object tracking via online discriminative appearance modeling EURASIP Journal on Advances in Signal Processing Visual tracking Mean shift Online learning Discriminative appearance |
| title | Robust object tracking via online discriminative appearance modeling |
| title_full | Robust object tracking via online discriminative appearance modeling |
| title_fullStr | Robust object tracking via online discriminative appearance modeling |
| title_full_unstemmed | Robust object tracking via online discriminative appearance modeling |
| title_short | Robust object tracking via online discriminative appearance modeling |
| title_sort | robust object tracking via online discriminative appearance modeling |
| topic | Visual tracking Mean shift Online learning Discriminative appearance |
| url | http://link.springer.com/article/10.1186/s13634-019-0646-0 |
| work_keys_str_mv | AT weiliu robustobjecttrackingviaonlinediscriminativeappearancemodeling AT xinsun robustobjecttrackingviaonlinediscriminativeappearancemodeling AT dongli robustobjecttrackingviaonlinediscriminativeappearancemodeling |