Motion imagesOne of the most common techniques when one works with motion analysis from video files is to start by creating what we call a motion image. The motion image is found by calculating the absolute pixel difference between subsequent frames in a video file, as illustrated in the figure below. The end result is an image in which only the pixels that have changed between the frames are displayed. The quality of the raw motion image depends on the quality of the original video stream. Small changes in lighting, camera motion, compression artefacts, and so on can influence the final image. Such visual interference can be eliminated using a simple low-pass filter to remove pixels below a certain threshold, or a more advanced “noise reduction” filter, as illustrated below. Either tool cleans up the image, leaving only the most salient parts of the activity in the motion. The video of the filtered motion image is usually the starting point for further processing and analysis of the video material.
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Music Moves: Why Does Music Make You Move?
MotiongramsThe motion-history images above reveal information about the spatial aspects of a motion sequence, but there is no information about the temporal unfolding of the motion. Then a motiongram may be useful, since it displays motion over time. A motiongram is created by averaging over a motion image, as illustrated in the figure below. This figure shows a schematic overview of the creation of motiongrams, based on a short recording of a piano performance. The horizontal motiongram clearly reveals the lifting of the hands, as well as some swaying in the upper part of the body. The vertical motiongram reveals the motion of the hands along the keyboard, here seen from the front, as in the previous figures. One example of the ways in which motiongrams can be used to study dance performance can be seen below. This display shows motion-average images and motiongrams of forty seconds of dance improvisation by three different dancers who are moving to the same musical material (approx. forty seconds). A spectrogram of the musical sound is displayed below the motiongrams. The motiongrams reveal spatiotemporal information that is not possible to convey using keyframe images, and they facilitate the researcher’s ability to follow the trajectories of the hands and heads of the dancers throughout the sequences. For example, the first dancer used quite similar motions for the three repeated excerpts in the sequence: a large, slow upwards motion in the arms, followed by a bounce. The third dancer, on the other hand, had more varied motions and covered the whole vertical plane with the arms. Such structural differences and similarities can be identified in the motiongrams, and then studied in more detail in the original video files.
From Music Research to Clinical PracticeWe can make a little detour at the end of this article. As researchers working on basic issues, we are often asked about the “usefulnes” of what we do. It is often difficult to answer this question, because our research is not meant to be useful in the first place. But sometimes seemingly “useless” developments can have an impact elsewhere. The visualisation techniques mentioned above have actually turned out to be very useful in medical research and clinical practice. A group of researchers in Trondheim, Norway, found that the motiongram technique was an excellent way of detecting so-called fidgety motion in infants. This is important when it comes to screening pre-term infants that are in the risk zone for developing cerebral palsy, as shown in this image with a healthy infant (top) and an infant with cerebral palsy (below).
- VideoAnalysis. A simple application that lets you import av video file, press a button, and you get motion images, motiongrams, and more.
- Musical Gestures Toolbox. This toolbox is available for the programming environments Max, Matlab and Jupyter, and provides the building blocks for making many different types of video visualizations.
- Adde, L., J. L. Helbostad, A. R. Jensenius, G. Taraldsen & R. Støen (2009). Using computer-based video analysis in the study of fidgety movements. Early Human Development 85(9), 541–547.
- Jensenius, A. R. (2007). Action–sound: developing methods and tools to study music-related body movement. Ph.D. thesis, University of Oslo.
- Levin, G. (2005). An informal catalogue of slit-scan video artworks.
- Marey, E.-J. (1884). Analyse cinématique de la marche. cras, t. xcviii, séance du 19 mai 1884.
Music Moves: Why Does Music Make You Move?
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