Research and development trend of machine learning based track data association algorithm

Xuanyue Zhu; Haoxin Cai; Yue Cheng; Yongqiang Liu; Guanfeng Yu

doi:10.1117/12.2673248

14 April 2023 Research and development trend of machine learning based track data association algorithm

Xuanyue Zhu, Haoxin Cai, Yue Cheng, Yongqiang Liu, Guanfeng Yu

Author Affiliations +

Proceedings Volume 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022); 126130Z (2023) https://doi.org/10.1117/12.2673248
Event: International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 2022, Chongqing, China

Abstract

As the number of the measurement target sensors continues to grow and complicated, and the original track data association algorithm is complexity of the target increases, the process of track association is gradually difficult to meet the actual demand, so it is important to further analyze the multiple target tracking data association algorithm. Machine learning has become a research hotspot in various fields in recent years. In the field of data association, machine learning algorithms can also be used to achieve the processing of track data. Machine learning algorithms can effectively improve the correct association rate compared to traditional methods. Firstly, the basic content of the track data association problem is described; secondly, two common types of research under the multiple target tracking data association problems based on machine learning are analyzed and summarized; after that, the future development trend is predicted given the current research status; finally, the overall research content is summarized.

Citation Download Citation

Xuanyue Zhu, Haoxin Cai, Yue Cheng, Yongqiang Liu, and Guanfeng Yu "Research and development trend of machine learning based track data association algorithm", Proc. SPIE 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 126130Z (14 April 2023); https://doi.org/10.1117/12.2673248

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