Jerk-Aware Video Acceleration Magnification

Takeda, Shoichiro; Okami, Kazuki; Mikami, Dan; Isogai, Megumi; Kimata, Hideaki

doi:10.1109/cvpr.2018.00190

Cited by 24 publications

(23 citation statements)

References 21 publications

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“…Despite its significance in many tasks, acceleration is often not explicitly modeled in many computer vision methods. However, there is a small body of literature that has considered how to recover (Edison & Jiji, 2017) and amplify optical acceleration (Zhang et al, 2017;Takeda et al, 2018). Given that acceleration can be equally as important as position and velocity in understanding dynamical systems, we argue that this topic deserves further attention.…”

Section: Introductionmentioning

confidence: 94%

Learning Higher-Order Dynamics in Video-Based Cardiac Measurement

Hill¹,

Liu²,

McDuff³

2021

Preprint

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Computer vision methods typically optimize for first-order dynamics (e.g., optical flow). However, in many cases the properties of interest are subtle variations in higher-order changes, such as acceleration. This is true in the cardiac pulse, where the second derivative can be used as an indicator of blood pressure and arterial disease. Recent developments in camera-based vital sign measurement have shown that cardiac measurements can be recovered with impressive accuracy from videos; however, the majority of research has focused on extracting summary statistics such as heart rate. Less emphasis has been put on the accuracy of waveform morphology that is necessary for many clinically impactful scenarios. In this work, we provide evidence that higher-order dynamics are better estimated by neural models when explicitly optimized for in the loss function. Furthermore, adding second-derivative inputs also improves performance when estimating second-order dynamics. By incorporating the second derivative of both the input frames and the target vital sign signals into the training procedure, our model is better able to estimate left ventricle ejection time (LVET) intervals.

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Section: Introductionmentioning

confidence: 94%

Learning Higher-Order Dynamics in Video-Based Cardiac Measurement

Hill¹,

Liu²,

McDuff³

2021

Preprint

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“…The technique presented in [ 55 ] expands the area of applications of video motion magnification to include observations of small motions in the presence of quick, large motions. The leading subject of the cited study is the elimination of the effect of quick, large motions expressed with significant changes in the acceleration.…”

Section: Motion Magnificationmentioning

confidence: 99%

Motion Magnification of Vibration Image in Estimation of Technical Object Condition-Review

Śmieja

Mamala

Prażnowski

et al. 2021

Sensors

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One of the most important features of the proper operation of technical objects is monitoring the vibrations of their mechanical components. The currently significant proportion of the research methods in this regard includes a group of research methods based on the conversion of vibrations using sensors providing data from individual locations. In parallel with the continuous improvement of these tools, new methods for acquiring information on the condition of the object have emerged due to the rapid development of visual systems. Their actual effectiveness determined the switch from research laboratories to actual industrial installations. In many cases, the application of the visualization methods can supplement the conventional methods applied and, under particular conditions, can effectively replace them. The decisive factor is their non-contact nature and the possibility for simultaneous observation of multiple points of the selected area. Visual motion magnification (MM) is an image processing method that involves the conscious and deliberate deformation of input images to the form that enables the visual observation of vibration processes which are not visible in their natural form. The first part of the article refers to the basic terms in the field of expressing motion in an image (based on the Lagrangian and Eulerian approaches), the formulation of the term of optical flow (OF), and the interpretation of an image in time and space. The following part of the article reviews the main processing algorithms in the aspect of computational complexity and visual quality and their modification for applications under specific conditions. The comparison of the MM methods presented in the paper and recommendations for their applications across a wide variety of fields were supported with examples originating from recent publications. The effectiveness of visual methods based on motion magnification in machine diagnosis and the identification of malfunctions are illustrated with selected examples of the implementation derived from authors’ workshop practice under industrial conditions.

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“…Similar to [11], [12], phase unwrapping is used to correct the unstable phase jumps due to the periodicity of the phase between [−π, π].…”

Section: S Lmentioning

confidence: 99%