Performance of correlational filtering and deep learning based single target tracking algorithms / ZhongMing Liao and Azlan Ismail

Visual target tracking is an important research element in the field of computer vision. The applications are very wide. In terms of the computer vision field, deep learning has achieved remarkable results. It has broken through many complex problems that are difficult to be solved by traditional al...

Full description

Saved in:
Bibliographic Details
Main Authors: ZhongMing, Liao (Author), Ismail, Azlan (Author)
Format: Book
Published: Universiti Teknologi MARA, Sarawak, 2023-03.
Subjects:
Online Access:Link Metadata
Tags: Add Tag
No Tags, Be the first to tag this record!

MARC

LEADER 00000 am a22000003u 4500
001 repouitm_79851
042 |a dc 
100 1 0 |a ZhongMing, Liao  |e author 
700 1 0 |a Ismail, Azlan  |e author 
245 0 0 |a Performance of correlational filtering and deep learning based single target tracking algorithms / ZhongMing Liao and Azlan Ismail 
260 |b Universiti Teknologi MARA, Sarawak,   |c 2023-03. 
500 |a https://ir.uitm.edu.my/id/eprint/79851/1/79851.pdf 
520 |a Visual target tracking is an important research element in the field of computer vision. The applications are very wide. In terms of the computer vision field, deep learning has achieved remarkable results. It has broken through many complex problems that are difficult to be solved by traditional algorithms. Therefore, reviewing the visual target tracking algorithms based on deep learning from different perspectives is important. This paper closely follows the tracking framework of target tracking algorithms and discusses in detail the traditional visual target tracking methods, the mainstream single target tracking algorithms based on correlation filtering, and the video single target tracking algorithms based on deep learning. Experiments were conducted on OTB100 and VOT2018 benchmark datasets, and the experimental data obtained were analyzed to derive two visual single-target tracking algorithms with optimal tracking performance. Finally, the future development of tracking algorithms is envisioned. 
546 |a en 
690 |a H Social Sciences (General) 
690 |a Research 
655 7 |a Article  |2 local 
655 7 |a PeerReviewed  |2 local 
787 0 |n https://ir.uitm.edu.my/id/eprint/79851/ 
787 0 |n https://jsst.uitm.edu.my/index.php/jsst 
856 4 1 |u https://ir.uitm.edu.my/id/eprint/79851/  |z Link Metadata