Feature- and depth-supported modified total variation optical flow for 3D motion field estimation in real scenes

CVPR 2011 ◽  
2011 ◽  
Author(s):  
Thomas Muller ◽  
Jens Rannacher ◽  
Clemens Rabe ◽  
Uwe Franke
Keyword(s):  
Optical Flow ◽  
Total Variation ◽  
Motion Field ◽  
3D Motion ◽  
Field Estimation

scholarly journals Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation

2018 ◽  
pp. 484-501 ◽  
Author(s):  
Zhaoyang Lv ◽  
Kihwan Kim ◽  
Alejandro Troccoli ◽  
Deqing Sun ◽  
James M. Rehg ◽  
...  
Keyword(s):  
Motion Field ◽  
Dynamic Scenes ◽  
Moving Camera ◽  
3D Motion ◽  
Field Estimation

scholarly journals An Empirical Study of Exhaustive Matching for Improving Motion Field Estimation

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 320 ◽  
Author(s):  
Vanel Lazcano
Keyword(s):  
Empirical Study ◽  
Optical Flow ◽  
Energy Model ◽  
Motion Field ◽  
Variational Models ◽  
Regularization Term ◽  
Additional Information ◽  
Optical Flow Method ◽  
Field Estimation ◽  
Data Term

Optical flow is defined as the motion field of pixels between two consecutive images. Traditionally, in order to estimate pixel motion field (or optical flow), an energy model is proposed. This energy model is composed of (i) a data term and (ii) a regularization term. The data term is an optical flow error estimation and the regularization term imposes spatial smoothness. Traditional variational models use a linearization in the data term. This linearized version of data term fails when the displacement of the object is larger than its own size. Recently, the precision of the optical flow method has been increased due to the use of additional information, obtained from correspondences computed between two images obtained by different methods such as SIFT, deep-matching, and exhaustive search. This work presents an empirical study in order to evaluate different strategies for locating exhaustive correspondences improving flow estimation. We considered a different location for matching random locations, uniform locations, and locations on maximum gradient magnitude. Additionally, we tested the combination of large and medium gradients with uniform locations. We evaluated our methodology in the MPI-Sintel database, which represents the state-of-the-art evaluation databases. Our results in MPI-Sintel show that our proposal outperforms classical methods such as Horn-Schunk, TV-L1, and LDOF, and our method performs similar to MDP-Flow.

Simulation on Vehicle Platform Based on Decoupled Optical Flow Motion Field Model

2019 ◽  
Vol 39 (4) ◽  
pp. 0415005
Author(s):  
乌萌 Wu Meng ◽  
郝金明 Hao Jinming ◽  
付浩 Fu Hao ◽  
高扬 Gao Yang
Keyword(s):  
Optical Flow ◽  
Field Model ◽  
Motion Field ◽  
Flow Motion ◽  
Vehicle Platform
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