Face landmark point tracking using LK pyramid optical flow

2018 ◽  
Author(s):  
Gang Zhang ◽  
Sikan Tang ◽  
Jiaquan Li
Keyword(s):  
Optical Flow ◽  
Landmark Point ◽  
Point Tracking

Analysis of Human Accident Video Using Optical Flow With Physically-Based Constraints

2013 ◽  
Author(s):  
Christine Zwart ◽  
David Frakes ◽  
William Singhose
Keyword(s):  
Optical Flow ◽  
Poor Quality ◽  
Logical Approach ◽  
Automated Tracking ◽  
Motion Data ◽  
Point Tracking ◽  
Points Of Interest ◽  
Physically Based ◽  
Poor Lighting

Videos that capture accidents are usually of poor quality: they are likely to be taken from a bad angle, with poor lighting, and contain occluded points. However, the motion data contained in such videos can be very valuable for understanding and preventing accidents. To extract the motion of a body from a video: 1) the points of interest must be identified and 2) point tracking from frame-to-frame must be accomplished. Accordingly, one logical approach is to focus on automated tracking, while allowing a human to identify important points of interest [1].

Motion correction in PET/CT

2005 ◽  
Vol 44 (S 01) ◽  
pp. S46-S50 ◽  
Author(s):  
M. Dawood ◽  
N. Lang ◽  
F. Büther ◽  
M. Schäfers ◽  
O. Schober ◽  
...  
Keyword(s):  
Optical Flow ◽  
Motion Correction ◽  
Motion Vector ◽  
Emission Tomography ◽  
Cardiac Pet ◽  
Novel Approach ◽  
Positron Emission ◽  
Pet Ct ◽  
Flow Algorithm ◽  
Motion Vector Field

Summary:Motion in PET/CT leads to artifacts in the reconstructed PET images due to the different acquisition times of positron emission tomography and computed tomography. The effect of motion on cardiac PET/CT images is evaluated in this study and a novel approach for motion correction based on optical flow methods is outlined. The Lukas-Kanade optical flow algorithm is used to calculate the motion vector field on both simulated phantom data as well as measured human PET data. The motion of the myocardium is corrected by non-linear registration techniques and results are compared to uncorrected images.

Locating Method for Vehicle at Intersection Based on TPVM and Optical Flow

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Tao Chen ◽  
Linkun Fan ◽  
Xuchuan Li ◽  
Congshuai Guo ◽  
Miaomiao Qiao
Keyword(s):  
Optical Flow
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