Back to Search
Start Over
Deep learning-based multiple particle tracking in complex system
- 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.
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