Hierarchical Motion Field Alignment for Robust Optical Flow Estimation
Optical flow estimation is a fundamental and long-standing task in computer vision, facilitating the understanding of motion within visual scenes. In this study, we aim to improve optical flow estimation, particularly in challenging scenarios involving small and fast-moving objects. Specifically, we...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/9/2653 |
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| author | Dianbo Ma Kousuke Imamura Ziyan Gao Xiangjie Wang Satoshi Yamane |
| author_facet | Dianbo Ma Kousuke Imamura Ziyan Gao Xiangjie Wang Satoshi Yamane |
| author_sort | Dianbo Ma |
| collection | DOAJ |
| description | Optical flow estimation is a fundamental and long-standing task in computer vision, facilitating the understanding of motion within visual scenes. In this study, we aim to improve optical flow estimation, particularly in challenging scenarios involving small and fast-moving objects. Specifically, we proposed a learning-based model incorporating two key components: the Hierarchical Motion Field Alignment module, which ensures accurate estimation of objects of varying sizes while maintaining manageable computational complexity, and the Correlation Self-Attention module, which effectively handles large displacements, making the model suitable for scenarios with fast-moving objects. Additionally, we introduced a Multi-Scale Correlation Search layer to enhance the four-dimensional cost volume, enabling the model to address various types of motion. Experimental results demonstrate that our model achieves superior generalization performance and significantly improves the estimation of small, fast-moving objects. |
| format | Article |
| id | doaj-art-c5b53d0eeccb472fa05225f2a2c45b72 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-c5b53d0eeccb472fa05225f2a2c45b722025-08-20T02:31:08ZengMDPI AGSensors1424-82202025-04-01259265310.3390/s25092653Hierarchical Motion Field Alignment for Robust Optical Flow EstimationDianbo Ma0Kousuke Imamura1Ziyan Gao2Xiangjie Wang3Satoshi Yamane4Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa 9201192, JapanGraduate School of Natural Science and Technology, Kanazawa University, Kanazawa 9201192, JapanSchool of Information Science, Japan Advanced Institute of Science and Technology, Nomi 9231292, JapanState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, ChinaGraduate School of Natural Science and Technology, Kanazawa University, Kanazawa 9201192, JapanOptical flow estimation is a fundamental and long-standing task in computer vision, facilitating the understanding of motion within visual scenes. In this study, we aim to improve optical flow estimation, particularly in challenging scenarios involving small and fast-moving objects. Specifically, we proposed a learning-based model incorporating two key components: the Hierarchical Motion Field Alignment module, which ensures accurate estimation of objects of varying sizes while maintaining manageable computational complexity, and the Correlation Self-Attention module, which effectively handles large displacements, making the model suitable for scenarios with fast-moving objects. Additionally, we introduced a Multi-Scale Correlation Search layer to enhance the four-dimensional cost volume, enabling the model to address various types of motion. Experimental results demonstrate that our model achieves superior generalization performance and significantly improves the estimation of small, fast-moving objects.https://www.mdpi.com/1424-8220/25/9/2653attention mechanismscomputer visioncorrelationdeep learningimage processingmotion estimation |
| spellingShingle | Dianbo Ma Kousuke Imamura Ziyan Gao Xiangjie Wang Satoshi Yamane Hierarchical Motion Field Alignment for Robust Optical Flow Estimation Sensors attention mechanisms computer vision correlation deep learning image processing motion estimation |
| title | Hierarchical Motion Field Alignment for Robust Optical Flow Estimation |
| title_full | Hierarchical Motion Field Alignment for Robust Optical Flow Estimation |
| title_fullStr | Hierarchical Motion Field Alignment for Robust Optical Flow Estimation |
| title_full_unstemmed | Hierarchical Motion Field Alignment for Robust Optical Flow Estimation |
| title_short | Hierarchical Motion Field Alignment for Robust Optical Flow Estimation |
| title_sort | hierarchical motion field alignment for robust optical flow estimation |
| topic | attention mechanisms computer vision correlation deep learning image processing motion estimation |
| url | https://www.mdpi.com/1424-8220/25/9/2653 |
| work_keys_str_mv | AT dianboma hierarchicalmotionfieldalignmentforrobustopticalflowestimation AT kousukeimamura hierarchicalmotionfieldalignmentforrobustopticalflowestimation AT ziyangao hierarchicalmotionfieldalignmentforrobustopticalflowestimation AT xiangjiewang hierarchicalmotionfieldalignmentforrobustopticalflowestimation AT satoshiyamane hierarchicalmotionfieldalignmentforrobustopticalflowestimation |