Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/25082
Title: Scale adaptive region covariance descriptor for visual tracking
Authors: Abu Hassan M.F.
Pri A.S.
Ahmad Z.
Tuan Dir T.M.A.
Issue Date: 2020
Publisher: IOP Conference Series: Materials Science and Engineering
Citation: Abu 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.
Abstract: This 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.
Description: This article index by Scopus
URI: http://hdl.handle.net/123456789/25082
Appears in Collections:Conference Papers

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