| Autor: |
Yifan Wang, Wu Wang, Yang Li, Jinshi Guo, Yu Xu, Jiaqi Ma, Yu Ling, Yanan Fu, Yaodong Jia |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Journal of King Saud University: Computer and Information Sciences, Vol 36, Iss 4, Pp 102029- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1319-1578 |
| DOI: |
10.1016/j.jksuci.2024.102029 |
| Popis: |
Optical flow estimation captures the motion information of objects in a scene through analyzing the displacement of pixels in an image over time. This technology provides a powerful tool for vision systems, allowing them to understand and perceive changes in dynamic environments. Optical flow estimation has a wide range of applications in fields such as military, medicine, traffic regulation, and intelligent robotics. This study systematically explores two key directions in the field of optical flow estimation—traditional methods and emerging strategies based on deep learning—aiming to provide a comprehensive and in-depth perspective to help scholars gain a deeper understanding of the development of the optical flow estimation field. First, the core principles and constraints of conventional optical flow estimation are briefly analyzed, focusing on reviewing the faced challenges and associated solutions based on differential, variational, and matching optical flow estimation principles. Then, we discuss the backbone networks and training strategies used in deep learning approaches in depth, with a particular focus on the current challenges faced under supervised and unsupervised conditions, as well as existing solutions. In addition, to evaluate the performance of these methods, existing datasets and evaluation indicators are analyzed and comprehensive comparisons on several publicly available datasets are conducted. Finally, we discuss prospects related to various application fields and future research directions in the field of optical flow estimation. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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