SU-FF-J-24: An Optical Flow Based Motion Tracking Method Using Fluoroscopic Video

2006 ◽  
Vol 33 (6Part5) ◽  
pp. 2025-2025 ◽  
Author(s):  
Q Xu ◽  
R Hamilton ◽  
S Jiang
Keyword(s):  
Optical Flow ◽  
Motion Tracking ◽  
Tracking Method ◽  
J 24

scholarly journals Feature Point Extraction and Motion Tracking of Cardiac Color Ultrasound under Improved Lucas–Kanade Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaoli Zhang ◽  
Punan Li ◽  
Yibing Li
Keyword(s):  
Optical Flow ◽  
Speckle Tracking ◽  
Motion Tracking ◽  
Color Doppler ◽  
Color Doppler Ultrasound ◽  
Feature Points ◽  
Sift Algorithm ◽  
Calculation Time ◽  
Tracking Method ◽  
Optical Flow Method

The purpose of this research is to study the application effect of Lucas–Kanade algorithm in right ventricular color Doppler ultrasound feature point extraction and motion tracking under the condition of scale invariant feature transform (SIFT). This study took the right ventricle as an example to analyze the extraction effect and calculation rate of SIFT algorithm and improved Lucas–Kanade algorithm. It was found that the calculation time before and after noise removal by the SIFT algorithm was 0.49 s and 0.46 s, respectively, and the number of extracted feature points was 703 and 698, respectively. The number of feature points extracted by the SIFT algorithm and the calculation time were significantly better than those of other algorithms ( P < 0.01 ). The mean logarithm of the matching points of the SIFT algorithm for order matching and reverse order matching was 20.54 and 20.46, respectively. The calculation time and the number of feature points for the SIFT speckle tracking method were 1198.85 s and 81, respectively, and those of the optical flow method were 3274.19 s and 80, respectively. The calculation time of the SIFT speckle tracking method was significantly lower than that of the optical flow method ( P < 0.05 ), and there was no statistical difference in the number of feature points between the SIFT speckle tracking method and the optical flow method ( P > 0.05 ). In conclusion, the improved Lucas–Kanade algorithm based on SIFT significantly improves the accuracy of feature extraction and motion tracking of color Doppler ultrasound, which shows the value of the algorithm in the clinical application of color Doppler ultrasound.

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.

Algorithm Optimization of Motion Tracking Based on Optical Flow

2014 ◽  
Vol 926-930 ◽  
pp. 2938-2941
Author(s):  
Dong Ming Liu ◽  
Chao Liu ◽  
Hai Wei Mu
Keyword(s):  
Optical Flow ◽  
Motion Tracking ◽  
Differential Method ◽  
Video Image ◽  
Time Axis ◽  
Algorithm Optimization ◽  
Image Preprocessing ◽  
Smoothing Filter ◽  
Matching Filter ◽  
Non Gaussian

Optical flow is an important kind of video motion tracking algorithm, and Lucas-Kanade (LK) algorithm is an effective differential method in terms of calculating optical flow. The 3D Gaussian smoothing filter is properly introduced in the image preprocessing stage of the LK algorithm, which makes it possible to increase the correlation of the adjacent pixels in the time axis, improve the blur effect of the video image and overcome the 2D Gaussian filters disadvantage that is not suitable for the video image processing. More importantly, the optimized 3D non-Gaussian matching filter is chosen during the 3D derivative calculating, and it is capable of reducing the error rate of the velocity vector calculation and enhancing the calculation accuracy of the optical flow.

A Robust Facial Feature Tracking Method Based on Optical Flow and Prior Measurement

2010 ◽  
Vol 4 (4) ◽  
pp. 62-75
Author(s):  
Guoyin Wang ◽  
Yong Yang ◽  
Kun He
Keyword(s):  
Optical Flow ◽  
Feature Tracking ◽  
Facial Feature ◽  
Recognition System ◽  
Research Area ◽  
Expression Recognition ◽  
Tracking Method ◽  
Facial Feature Tracking ◽  
Recognition Result ◽  
Method Of Measurement

Cognitive informatics (CI) is a research area including some interdisciplinary topics. Visual tracking is not only an important topic in CI, but also a hot topic in computer vision and facial expression recognition. In this paper, a novel and robust facial feature tracking method is proposed, in which Kanade-Lucas-Tomasi (KLT) optical flow is taken as basis. The prior method of measurement consisting of pupils detecting features restriction and errors and is used to improve the predictions. Simulation experiment results show that the proposed method is superior to the traditional optical flow tracking. Furthermore, the proposed method is used in a real time emotion recognition system and good recognition result is achieved.

Fast motion tracking based on moth-flame optimization and kernel correlation filter

2020 ◽  
Vol 39 (3) ◽  
pp. 3825-3837
Author(s):  
Yibin Chen ◽  
Guohao Nie ◽  
Huanlong Zhang ◽  
Yuxing Feng ◽  
Guanglu Yang
Keyword(s):  
Motion Tracking ◽  
State Of The Art ◽  
Candidate Region ◽  
Correlation Filter ◽  
Data Sets ◽  
Search Window ◽  
Tracking Method ◽  
Fast Motion ◽  
Kernel Correlation

Kernel Correlation Filter (KCF) tracker has shown great potential on precision, robustness and efficiency. However, the candidate region used to train the correlation filter is fixed, so tracking is difficult when the target escapes from the search window due to fast motion. In this paper, an improved KCF is put forward for long-term tracking. At first, the moth-flame optimization (MFO) algorithm is introduced into tracking to search for lost target. Then, the candidate sample strategy of KCF tracking method is adjusted by MFO algorithm to make it has the capability of fast motion tracking. Finally, we use the conservative learning correlation filter to judge the moving state of the target, and combine the improved KCF tracker to form a unified tracking framework. The proposed algorithm is tested on a self-made dataset benchmark. Moreover, our method obtains scores for both the distance precision plot (0.891 and 0.842) and overlap success plots (0.631 and 0.601) on the OTB-2013 and OTB-2015 data sets, respectively. The results demonstrate the feasibility and effectiveness compared with the state-of-the-art methods, especially in dealing with fast or uncertain motion.

scholarly journals A Novel Change Detection Method for Natural Disaster Detection and Segmentation from Video Sequence

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5076
Author(s):  
Huijiao Qiao ◽  
Xue Wan ◽  
Youchuan Wan ◽  
Shengyang Li ◽  
Wanfeng Zhang
Keyword(s):  
Change Detection ◽  
Optical Flow ◽  
Natural Disaster ◽  
Motion Tracking ◽  
Video Sequence ◽  
Monitoring And Evaluation ◽  
Threshold Selection ◽  
Geometric Information ◽  
Flow Estimation ◽  
Key Steps

Change detection (CD) is critical for natural disaster detection, monitoring and evaluation. Video satellites, new types of satellites being launched recently, are able to record the motion change during natural disasters. This raises a new problem for traditional CD methods, as they can only detect areas with highly changed radiometric and geometric information. Optical flow-based methods are able to detect the pixel-based motion tracking at fast speed; however, they are difficult to determine an optimal threshold for separating the changed from the unchanged part for CD problems. To overcome the above problems, this paper proposed a novel automatic change detection framework: OFATS (optical flow-based adaptive thresholding segmentation). Combining the characteristics of optical flow data, a new objective function based on the ratio of maximum between-class variance and minimum within-class variance has been constructed and two key steps are motion detection based on optical flow estimation using deep learning (DL) method and changed area segmentation based on an adaptive threshold selection. Experiments are carried out using two groups of video sequences, which demonstrated that the proposed method is able to achieve high accuracy with F1 value of 0.98 and 0.94, respectively.

Sign in / Sign up

Export Citation Format

Share Document