Depth-Aware Motion Magnification

2016 ◽  
pp. 467-482 ◽  
Author(s):  
Julian F. P. Kooij ◽  
Jan C. van Gemert
Keyword(s):  
Motion Magnification

scholarly journals Frequency Variability Feature for Life Signs Detection and Localization in Natural Disasters

2021 ◽  
Vol 13 (4) ◽  
pp. 796
Author(s):  
Long Zhang ◽  
Xuezhi Yang ◽  
Jing Shen
Keyword(s):  
Natural Disasters ◽  
Current Method ◽  
Peak Frequency ◽  
Significant Information ◽  
Physiological Phenomenon ◽  
In The Wild ◽  
Motion Magnification ◽  
Background Motion ◽  
Detection And Localization ◽  
Frequency Properties

The locations and breathing signal of people in disaster areas are significant information for search and rescue missions in prioritizing operations to save more lives. For detecting the living people who are lying on the ground and covered with dust, debris or ashes, a motion magnification-based method has recently been proposed. This current method estimates the locations and breathing signal of people from a drone video by assuming that only human breathing-related motions exist in the video. However, in natural disasters, background motions, such as swing trees and grass caused by wind, are mixed with human breathing, that distort this assumption, resulting in misleading or even no life signs locations. Therefore, the life signs in disaster areas are challenging to be detected due to the undesired background motions. Note that human breathing is a natural physiological phenomenon, and it is a periodic motion with a steady peak frequency; while background motion always involves complex space-time behaviors, their peak frequencies seem to be variable over time. Therefore, in this work we analyze and focus on the frequency properties of motions to model a frequency variability feature used for extracting only human breathing, while eliminating irrelevant background motions in the video, which would ease the challenge in detection and localization of life signs. The proposed method was validated with both drone and camera videos recorded in the wild. The average precision measures of our method for drone and camera videos were 0.94 and 0.92, which are higher than that of compared methods, demonstrating that our method is more robust and accurate to background motions. The implications and limitations regarding the frequency variability feature were discussed.

scholarly journals Volumetric Motion Magnification: Subtle Motion Extraction from 4D Data

Measurement ◽  
2021 ◽  
pp. 109211
Author(s):  
Matthew Southwick ◽  
Zhu Mao ◽  
Christopher Niezrecki
Keyword(s):  
Motion Extraction ◽  
Motion Magnification

Stereoscopic Motion Magnification in Minimally-Invasive Robotic Prostatectomy

2016 ◽  
pp. 35-45
Author(s):  
A. Jonathan McLeod ◽  
John S. H. Baxter ◽  
Uditha Jayarathne ◽  
Stephen Pautler ◽  
Terry M. Peters ◽  
...  
Keyword(s):  
Minimally Invasive ◽  
Robotic Prostatectomy ◽  
Motion Magnification

Motion Magnification Analysis for structural monitoring of ancient constructions

Measurement ◽  
2018 ◽  
Vol 129 ◽  
pp. 375-380 ◽  
Author(s):  
Vincenzo Fioriti ◽  
Ivan Roselli ◽  
Angelo Tatì ◽  
Roberto Romano ◽  
Gerardo De Canio
Keyword(s):  
Structural Monitoring ◽  
Motion Magnification

scholarly journals Motion magnification analysis applied to the dynamic identification of historic constructions

2018 ◽  
Vol 364 ◽  
pp. 012001 ◽  
Author(s):  
Vincenzo Fioriti ◽  
Ivan Roselli ◽  
Angelo Tatì ◽  
Roberto Romano ◽  
Gerardo De Canio
Keyword(s):  
Dynamic Identification ◽  
Motion Magnification

20, 000-fps Visual Motion Magnification on Pixel-parallel Vision Chip

2019 ◽  
Author(s):  
Junxian He ◽  
Xichuan Zhou ◽  
Yingcheng Lin ◽  
Chonglei Sun ◽  
Cong Shi ◽  
...  
Keyword(s):  
Visual Motion ◽  
Vision Chip ◽  
Motion Magnification
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