2D Shape Measurement of Multiple Moving Objects by GMM Background Modeling and Optical Flow

2005 ◽  
pp. 789-795 ◽  
Author(s):  
Dongxiang Zhou ◽  
Hong Zhang
Keyword(s):  
Optical Flow ◽  
Moving Objects ◽  
Background Modeling ◽  
Shape Measurement

Robust Autonomous Car-Like Robot Tracking Based on Tracking-Learning-Detection

2014 ◽  
Vol 687-691 ◽  
pp. 564-571 ◽  
Author(s):  
Lin Bao Xu ◽  
Shu Ming Tang ◽  
Jin Feng Yang ◽  
Yan Min Dong
Keyword(s):  
Optical Flow ◽  
Fast Moving ◽  
Moving Objects ◽  
Tracking Algorithm ◽  
Robust Tracking ◽  
Symmetry Detection ◽  
Tracking Method ◽  
Robot Tracking ◽  
Improve Accuracy ◽  
First Time

This paper proposes a robust tracking algorithm for an autonomous car-like robot, and this algorithm is based on the Tracking-Learning-Detection (TLD). In this paper, the TLD method is extended to track the autonomous car-like robot for the first time. In order to improve accuracy and robustness of the proposed algorithm, a method of symmetry detection of autonomous car-like robot rear is integrated into the TLD. Moreover, the Median-Flow tracker in TLD is improved with a pyramid-based optical flow tracking method to capture fast moving objects. Extensive experiments and comparisons show the robustness of the proposed method.

Absolute and dynamic position and shape measurement of fast moving objects employing novel laser Doppler techniques

2008 ◽  
Author(s):  
Thorsten Pfister ◽  
Philipp Günther ◽  
Lars Büttner ◽  
Jürgen Czarske
Keyword(s):  
Fast Moving ◽  
Moving Objects ◽  
Laser Doppler ◽  
Shape Measurement ◽  
Doppler Techniques

New Method for Surface Shape Measurement Based on Brox Optical Flow Algorithm

2020 ◽  
Vol 40 (17) ◽  
pp. 1712004
Author(s):  
唐钰欣 Tang Yuxin ◽  
孙平 Sun Ping ◽  
代晴 Dai Qing ◽  
范超 Fan Chao ◽  
类智方 Lei Zhifang
Keyword(s):  
Optical Flow ◽  
Surface Shape ◽  
New Method ◽  
Shape Measurement ◽  
Flow Algorithm

scholarly journals Accurate moving object segmentation in unconstraint videos based on robust seed pixels selection

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142094727
Author(s):  
Wenlong Zhang ◽  
Xiaoliang Sun ◽  
Qifeng Yu
Keyword(s):  
Optical Flow ◽  
Moving Objects ◽  
Object Segmentation ◽  
Moving Object ◽  
Data Sets ◽  
Seed Selection ◽  
Current Frame ◽  
Moving Object Segmentation ◽  
Random Walker ◽  
Previous Frame

Due to the clutter background motion, accurate moving object segmentation in unconstrained videos remains a significant open problem, especially for the slow-moving object. This article proposes an accurate moving object segmentation method based on robust seed selection. The seed pixels of the object and background are selected robustly by using the optical flow cues. Firstly, this article detects the moving object’s rough contour according to the local difference in the weighted orientation cues of the optical flow. Then, the detected rough contour is used to guide the object and the background seed pixel selection. The object seed pixels in the previous frame are propagated to the current frame according to the optical flow to improve the robustness of the seed selection. Finally, we adopt the random walker algorithm to segment the moving object accurately according to the selected seed pixels. Experiments on publicly available data sets indicate that the proposed method shows excellent performance in segmenting moving objects accurately in unconstraint videos.

3D shape measurement of static and moving objects with adaptive spatiotemporal correlation

2014 ◽  
Vol 53 (31) ◽  
pp. 7507 ◽  
Author(s):  
Bastian Harendt ◽  
Marcus Große ◽  
Martin Schaffer ◽  
Richard Kowarschik
Keyword(s):  
Moving Objects ◽  
Shape Measurement ◽  
3D Shape ◽  
3D Shape Measurement ◽  
Spatiotemporal Correlation

BAYESIAN VIDEO MOSAICING WITH MOVING OBJECTS

2002 ◽  
Vol 16 (03) ◽  
pp. 341-348 ◽  
Author(s):  
LLUIS BARCELÓ ◽  
XAVIER BINEFA
Keyword(s):  
Optical Flow ◽  
Main Part ◽  
Moving Objects ◽  
Video Mosaicing ◽  
Parametric Motion ◽  
Moving Parts

This paper presents a framework that creates background, foreground and a temporal summarization of the motions in a scene. The method is based on the Dominant Motion Assumption (DMA), where the background has a parametric motion and occupies the main part of the scene. Under this assumption, we present a robust optical flow based method to extract the moving parts of the scene using the clustering capabilities of mixtures of Gaussians. A general mosaicing method to summarize the background, the foreground and the trajectories of objects in the scene is also presented.

scholarly journals An Improved Optical Flow Algorithm Based on Mask-R-CNN and K-Means for Velocity Calculation

2019 ◽  
Vol 9 (14) ◽  
pp. 2808 ◽  
Author(s):  
Yahui Peng ◽  
Xiaochen Liu ◽  
Chong Shen ◽  
Haoqian Huang ◽  
Donghua Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Optical Flow ◽  
Navigation System ◽  
Moving Objects ◽  
Harmful Effect ◽  
Improved Method ◽  
Vision Navigation ◽  
Vehicle Velocity ◽  
Flow Algorithm

Aiming at enhancing the accuracy and reliability of velocity calculation in vision navigation, an improved method is proposed in this paper. The method integrates Mask-R-CNN (Mask Region-based Convolutional Neural Network) and K-Means with the pyramid Lucas Kanade algorithm in order to reduce the harmful effect of moving objects on velocity calculation. Firstly, Mask-R-CNN is used to recognize the objects which have motions relative to the ground and covers them with masks to enhance the similarity between pixels and to reduce the impacts of the noisy moving pixels. Then, the pyramid Lucas Kanade algorithm is used to calculate the optical flow value. Finally, the value is clustered by the K-Means algorithm to abandon the outliers, and vehicle velocity is calculated by the processed optical flow. The prominent advantages of the proposed algorithm are (i) decreasing the bad impacts to velocity calculation, due to the objects which have relative motions; (ii) obtaining the correct optical flow sets and velocity calculation outputs with less fluctuation; and (iii) the applicability enhancement of the optical flow algorithm in complex navigation environment. The proposed algorithm is tested by actual experiments. Results with superior precision and reliability show the feasibility and effectiveness of the proposed method for vehicle velocity calculation in vision navigation system.

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