Robust long-term target tracking algorithm based on objectness-guided tracking

Dan Tian; Yuxin Wang; Ying Hao

doi:10.1117/12.3014914

4 March 2024 Robust long-term target tracking algorithm based on objectness-guided tracking

Dan Tian, Yuxin Wang, Ying Hao

Proceedings Volume 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023); 1298164 (2024) https://doi.org/10.1117/12.3014914
Event: 9th International Symposium on Sensors, Mechatronics, and Automation (ISSMAS 2023), 2023, Nanjing, China

Abstract

Compared with short-term target tracking, long-term target tracking requires processing of long video sequences and scenes in which the target disappears and reappears. These scenarios are closer to reality, so the research of long-term target tracking algorithms is more practical. In this paper, the long-term target tracking algorithm is divided into two parts, local tracking and global detection. The local tracking module adopts Siam RPN framework, which can not only give the optimal candidate when the target exists, but also accurately determine the target does not exist when the target disappears and enter the global detection module. For the global detection, we propose a new global detection module, which directly predicts the size and position of the target on the whole image based on the target guidance, solves the problem of the original block technology, and greatly improves the tracking speed and accuracy. A large number of experimental results on the VOT-2018LT benchmark show that the proposed method achieves the best performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dan Tian, Yuxin Wang, and Ying Hao "Robust long-term target tracking algorithm based on objectness-guided tracking", Proc. SPIE 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023), 1298164 (4 March 2024); https://doi.org/10.1117/12.3014914

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
5 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Detection and tracking algorithms

Target detection

Image enhancement

Chemical species

Deep learning

Education and training

Video

Show All Keywords

Keywords/Phrases

Search In:

Publication Years