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 Paper |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Scale adaptive region covariance descriptor for visual tracking.pdf | 31.72 kB | Adobe PDF | View/Open Request a copy |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.