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dc.contributor.authorDung, Lanrongen_US
dc.contributor.authorWang, Shih-Chien_US
dc.date.accessioned2019-12-13T01:09:14Z-
dc.date.available2019-12-13T01:09:14Z-
dc.date.issued2020-01-01en_US
dc.identifier.isbn978-3-030-17798-0; 978-3-030-17797-3en_US
dc.identifier.issn2194-5357en_US
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-030-17798-0_57en_US
dc.identifier.urihttp://hdl.handle.net/11536/152988-
dc.description.abstractThis paper presents an object-tracking algorithm with multiple randomly-generated features. We intent to improve the compressive tracking method whose results are fluctuated between good and bad. Because the compressive tracking method generates the image features randomly, the resulting image features varies from time to time. The object tracker might fail for missing some significant features. Therefore, the results of traditional compressive tracking are unstable. To solve the problem, the proposed approach generates multiple features randomly and chooses the best tracking results by measuring the similarity for each candidate. In this paper, we use the Bhattacharyya coefficient as the similarity measurement. The experimental results show that the proposed tracking algorithm can greatly reduce the tracking errors. The best performance improvements in terms of center location error, bounding box overlap ratio, and success rate are from 63.62 pixels to 15.45 pixels, from 31.75% to 64.48%, and from 38.51% to 82.58%, respectively.en_US
dc.language.isoen_USen_US
dc.subjectObject trackingen_US
dc.subjectFeature extractionen_US
dc.subjectCompressive trackingen_US
dc.titleAn Improved Compressive Tracking Approach Using Multiple Random Feature Extraction Algorithmen_US
dc.typeProceedings Paperen_US
dc.identifier.doi10.1007/978-3-030-17798-0_57en_US
dc.identifier.journalADVANCES IN COMPUTER VISION, VOL 2en_US
dc.citation.volume944en_US
dc.citation.spage724en_US
dc.citation.epage733en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.identifier.wosnumberWOS:000490760000056en_US
dc.citation.woscount0en_US
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