Rigid body segmentation and shape description from dense optical flow under weak perspective

1997 ◽  
Vol 19 (2) ◽  
pp. 139-143 ◽  
Author(s):  
J. Weber ◽  
J. Malik
Keyword(s):  
Rigid Body ◽  
Optical Flow ◽  
Shape Description ◽  
Dense Optical Flow ◽  
Body Segmentation

MODIFIED METHOD FOR REAL-TIME COMPUTING DENSE OPTICAL FLOW ON FPGA

2019 ◽  
Vol 70 ◽  
pp. 146-157
Author(s):  
A. V. Bratulin ◽  
◽  
M. B. Nikiforov ◽  
A. I. Efimov ◽  
◽  
...  
Keyword(s):  
Optical Flow ◽  
Real Time ◽  
Modified Method ◽  
Dense Optical Flow ◽  
Real Time Computing

Image Stabilization Based on Panoramic Dense Optical Flow Matching for Camera on Flapping Wing

2021 ◽  
Author(s):  
Tian Shen ◽  
Cui Long ◽  
Liu Zhaoming ◽  
Wang Hongwei ◽  
Zhang Feng ◽  
...  
Keyword(s):  
Optical Flow ◽  
Flapping Wing ◽  
Image Stabilization ◽  
Dense Optical Flow

scholarly journals A Hardware-Friendly Optical Flow-Based Time-to-Collision Estimation Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 807
Author(s):  
Cong Shi ◽  
Zhuoran Dong ◽  
Shrinivas Pundlik ◽  
Gang Luo
Keyword(s):  
Embedded System ◽  
Optical Flow ◽  
Random Forests ◽  
Visual Motion ◽  
Hardware Acceleration ◽  
Ground Truth ◽  
Estimation Algorithm ◽  
Estimation Accuracy ◽  
Time To Collision ◽  
Dense Optical Flow

This work proposes a hardware-friendly, dense optical flow-based Time-to-Collision (TTC) estimation algorithm intended to be deployed on smart video sensors for collision avoidance. The algorithm optimized for hardware first extracts biological visual motion features (motion energies), and then utilizes a Random Forests regressor to predict robust and dense optical flow. Finally, TTC is reliably estimated from the divergence of the optical flow field. This algorithm involves only feed-forward data flows with simple pixel-level operations, and hence has inherent parallelism for hardware acceleration. The algorithm offers good scalability, allowing for flexible tradeoffs among estimation accuracy, processing speed and hardware resource. Experimental evaluation shows that the accuracy of the optical flow estimation is improved due to the use of Random Forests compared to existing voting-based approaches. Furthermore, results show that estimated TTC values by the algorithm closely follow the ground truth. The specifics of the hardware design to implement the algorithm on a real-time embedded system are laid out.

The MG/OPT algorithm for dense optical flow

2011 ◽  
Author(s):  
El Mostafa Kalmoun ◽  
Luis Garrido ◽  
Vicent Caselles
Keyword(s):  
Optical Flow ◽  
Dense Optical Flow

scholarly journals Optical Flow Estimation with Occlusion Detection

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 92
Author(s):  
Song Wang ◽  
Zengfu Wang
Keyword(s):  
Optical Flow ◽  
Feature Matching ◽  
Estimation Algorithm ◽  
Occlusion Detection ◽  
Multiple Sources ◽  
Flow Estimation ◽  
Optical Flow Estimation ◽  
Dense Optical Flow ◽  
Video Frames ◽  
Optimization Function

The dense optical flow estimation under occlusion is a challenging task. Occlusion may result in ambiguity in optical flow estimation, while accurate occlusion detection can reduce the error. In this paper, we propose a robust optical flow estimation algorithm with reliable occlusion detection. Firstly, the occlusion areas in successive video frames are detected by integrating various information from multiple sources including feature matching, motion edges, warped images and occlusion consistency. Then optimization function with occlusion coefficient and selective region smoothing are used to obtain the optical flow estimation of the non-occlusion areas and occlusion areas respectively. Experimental results show that the algorithm proposed in this paper is an effective algorithm for dense optical flow estimation.

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