Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/25082
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dc.contributor.authorAbu Hassan M.F.-
dc.contributor.authorPri A.S.-
dc.contributor.authorAhmad Z.-
dc.contributor.authorTuan Dir T.M.A.-
dc.date.accessioned2021-08-11T06:27:05Z-
dc.date.available2021-08-11T06:27:05Z-
dc.date.issued2020-
dc.identifier.citationAbu Hassan, M.F., Pri, A.S., Ahmad, Z., Tuan Dir, T.M.A. Scale adaptive region covariance descriptor for visual tracking (2020) IOP Conference Series: Materials Science and Engineering, 932 (1). DOI: 10.1088/1757-899X/932/1/012090.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/25082-
dc.descriptionThis article index by Scopusen_US
dc.description.abstractThis paper presents an adaptive approach for scale estimation in a tracking-by detection framework. The proposed method works by learning covariance descriptor based on multi-layer instance search region. Our results show that the proposed approach significantly improves the performance in term of detection rate compared to region covariance descriptor with using a fixed bounding box (single scale). From this work, it is believed that we have constructed a greater solution in choosing best layer for this descriptor, permitting to move forward to the next issues such as fast motion or motion blur for achieving a robust tracking system.en_US
dc.publisherIOP Conference Series: Materials Science and Engineeringen_US
dc.titleScale adaptive region covariance descriptor for visual trackingen_US
dc.conference.year2020en_US
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