Lightweight Optical Flow Estimation Using 1D Matching
Recent advancements in optical flow estimation have led to notable performance gains, driven by the adoption of transformer architectures, enhanced data augmentation, self-supervised learning techniques, the use of multiple video frames and iterative refinement of estimate optical flows. Nonetheless...
Main Authors: | Wonyong Seo, Woonsung Park, Munchurl Kim |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10477999/ |
Similar Items
-
Aggregating Different Scales of Attention on Feature Variants for Tomato Leaf Disease Diagnosis from Image Data: A Transformer Driven Study
by: Shahriar Hossain, et al.
Published: (2023-04-01) -
Axial Constraints for Global Matching-Based Optical Flow Estimation
by: Euiyeon Kim, et al.
Published: (2023-01-01) -
Accurate Realtime Motion Estimation Using Optical Flow on an Embedded System
by: Anis Ammar, et al.
Published: (2021-09-01) -
Optical Flow Estimation Based on Curvelet Transform and Spatio-temporal Derivatives
by: Atheer A. Sabri
Published: (2010-06-01) -
Pix2Pix-Based Monocular Depth Estimation for Drones with Optical Flow on AirSim
by: Tomoyasu Shimada, et al.
Published: (2022-03-01)