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Deep learning-based multiple particle tracking in complex system

Authors :
Xiaoming Xu
Jianjun Wei
Sheng Sang
Source :
AIP Advances, Vol 14, Iss 1, Pp 015049-015049-5 (2024)
Publication Year :
2024
Publisher :
AIP Publishing LLC, 2024.

Abstract

This paper presents an innovative approach for multiple particle tracking within complex systems, utilizing convolutional neural networks in conjunction with multi-output models. Accurate particle tracking is a critical prerequisite for unraveling the dynamic behaviors of particles in a myriad of research domains, encompassing colloidal particles, biological cells, and molecular dynamics. Different from conventional methodologies, our approach combines data preprocessing, multilayer perceptron model training, and multi-output model integration to yield precise and efficient particle tracking results. The significance of this research lies in the adaptability and versatility of the trained models, which are designed to surmount challenges, including crowded and noisy environments. This work represents a substantial step forward in particle tracking methodologies, providing a robust and efficient alternative to conventional methods, promising more profound investigations into particle dynamics within complex systems, and contributing to a deeper understanding of the microscale world.

Subjects

Subjects :
Physics
QC1-999

Details

Language :
English
ISSN :
21583226
Volume :
14
Issue :
1
Database :
Directory of Open Access Journals
Journal :
AIP Advances
Publication Type :
Academic Journal
Accession number :
edsdoj.3d0c9bda0ac844079ef96b3174ae18c9
Document Type :
article
Full Text :
https://doi.org/10.1063/5.0186670