Eulerian video magnification for revealing subtle changes in the world

Abstract

and output (bottom) videos plotted over time shows how our method amplifies the periodic color variation. In the input sequence the signal is imperceptible, but in the magnified sequence the variation is clear. The complete sequence is available in the supplemental video. AbstractOur goal is to reveal temporal variations in videos that are difficult or impossible to see with the naked eye and display them in an indicative manner. Our method, which we call Eulerian Video Magnification, takes a standard video sequence as input, and applies spatial decomposition, followed by temporal filtering to the frames. The resulting signal is then amplified to reveal hidden information. Using our method, we are able to visualize the flow of blood as it fills the face and also to amplify and reveal small motions. Our technique can run in real time to show phenomena occurring at temporal frequencies selected by the user.

Riesz pyramids for fast phase-based video magnification

“…Motion estimation, however, remains a challenging and computationally intensive task. Eulerian video magnification, introduced by Wu et al [21], is able to amplify small motions in videos without explicitly computing optical flow. In their work, the temporal brightness changes in frame sub-bands are amplified to amplify motions.…”

“…These motions require computational amplification to be revealed [10,12,19,21]. Manipulating the local phase in coefficients of a complex steerable pyramid decomposition of an image sequence is an effective, robust method of amplifying small motions in video [19], but complex steerable pyramids are very overcomplete (21 times) and costly to construct, requiring either a large number of filter taps or a frequency domain construction where care must be taken to avoid spatial wrap-around artifacts [11,15].…”

“…In summary, we present a new representation that can be used for video magnification that is (a) less overcom- [21] (b) and two phase-based methods, first with an eight orientation octavebandwidth complex steerable pyramid [19] (c), and second with our new Riesz pyramid (d). The quality of the video produced using our new representation (d) is comparable to that produced using the complex steerable pyramid method (c), but is approximately four times faster to compute.…”

RSS-based respiratory rate monitoring using periodic Gaussian processes and Kalman filtering

“…Such sensors are unsuitable in home healthcare applications since the person's mobility is restricted, an elderly suffering from dementia may forget to wear a sensor, and infants may remove them. An alternative is to use non-invasive methods that do not rely on body contact and popular approaches include vision-based techniques [3] and radio frequency (RF)-based methods [4]- [6], a research topic we explore in this work.…”

How Can We Improve Outcomes Assessment?

“…As an example of the capabilities of such systems, one recent image analysis study measured heart rate from standard-quality video based on small fluctuations in facial skin tone. 9 Although few studies have employed these methods within orthopedics, 10 they offer the promise of real-time motion analysis outside of the controlled laboratory setting. 11,12 Much work remains to be done, but more mobile quantitative functional assessment may soon be available for clinical research use.…”