As part of my year-long StillStanding project, I post an average image of the spherical video recordings on Mastodon daily. Average images The average image is similar to an “open shutter” technique in photography; it overlays all the frames in a video. The result is an image that shows the most prominent parts of the video recording. This is ideal for my StillStanding recordings, because the technique effectively “removes” objects that appear in the recording for a short period of time.

I participated in the conference The Aesthetics of Absence in Music of the Twenty-First Century at the Department of Musicology the last couple of days. Judith Lochhead started her keynote lecture with a clip from the movie The art of flying by Jan van Ijken. This is a beautiful short film based on clips of flocking birds: The art of flying from Jan van IJken on Vimeo. Of course, I wanted to see how some video visualizations would work, so I reached for the Musical Gestures Toolbox for Python.

I have been exploring different video visualizations as part of my annual stillstanding project. Some of these I post as part of my daily Mastodon updates, while others I only test for future publications. Most of the video visualizations and analyses are made with the Musical Gestures Toolbox for Python and structured as Jupyter Notebooks. I have been pondering whether skipping frames is a good idea. The 360-degree videos that I create visualizations from are shot at 25 fps.

Musique de Table is a wonderful piece written by Thierry de Mey. I have seen it performed live several times, and here came across a one-shot video recording that I thought it would be interesting to analyse: The test with some video visualization tools in the Musical Gestures Toolbox for Python. For running the commands below, you first need to import the toolbox in Python: import musicalgestures as mg I started the process by importing the source video:

After exploring some visualizations of kayaking, I was eager to see how a similar approach could work for walking. On a trip to the Norwegian mountains, specifically at Haugastøl, situated halfway between Oslo and Bergen, I strapped a GoPro Hero Black 10 on my chest and walked up and down a nearby hill called Storevarden. The walk was approximately 25 minutes up and down, and a fast-forward version of the video can be seen here:

Last year, I wrote about video-based motion analysis of kayaking. Those videos were recorded with a GoPro Hero 8 and I tested some of the video visualization methods of the Musical Gestures Toolbox for Python. This summer I am testing out some 360 cameras for my upcoming AMBIENT project. I thought I should take one of these, a GoPro Max, out for some kayaking in the Oslo fjord. Here are some impressions of the trip (and recording).

Like many others, I bought a kayak during the pandemic, and I have had many nice trips in the Oslo fiord over the last year. Working at RITMO, I think a lot about rhythm these days, and the rhythmic nature of kayaking made me curious to investigate the pattern a little more. Capturing kayaking motion My spontaneous investigations into kayak motion began with simply recording a short video of myself kayaking.

I came across a fantastic performance of a Bach prelude played on Boomwhackers by Les Objets Volants. https://www.youtube.com/watch?v=Y5seI0eJZCg It is really incredible how they manage to coordinate the sticks and make it into a beautiful performance. Given my interest in the visual aspects of music performance, I reached for the Musical Gestures Toolbox to create some video visualisations. I started with creating an average image of the video: This image is not particularly interesting.

In some recent posts I have explored the creation of motiongrams and average images, multi-exposure displays, and image masks. In this blog post I will explore different ways of generating keyframe displays using the very handy command line tool FFmpeg. As in the previous posts, I will use a contemporary dance video from the AIST Dance Video Database as an example: The first attempt is to create a 3x3 grid image by just sampling frames from the original image.

As part of my exploration in creating multi-exposure keyframe image displays with FFmpeg and ImageMagick, I tried out a number of things that did not help solve the initial problem but still could be interesting for other things. Most interesting was the automagic creation of image masks from a video file. I will use a contemporary dance video from the AIST Dance Video Database as an example: The first step is to extract keyframes from the video file using this one-liner ffmpeg command:

Researchers from AIST have released an open database of dance videos, and I got very excited to try out some visualization methods on some of the files. This was also a good chance to test out some new functionality in the Musical Gestures Toolbox for Matlab that we are developing at RITMO. The AIST collection contains a number of videos. I selected one hip-hop dance video based on a very steady rhythmic pattern, and a contemporary dance video that is more fluid in both motion and music.

Yesterday, I posted a blog entry on my TimeLapser application, and how it was used to document the working process of the making of the sculpture Hommage til kaffeselskapene by my mother. The final timelapse video looks like this: Now I have run this timelapse video through my VideoAnalysis application, to see what types of analysis material can come out of such a video. The average image displays a “summary” of the entire video recording, somehow similar to an “open shutter” in traditional photography.

Today the MIT Press journal Leonardo has published my paper entitled “Some video abstraction techniques for displaying body movement in analysis and performance”. The paper is a summary of my work on different types of visualisation techniques of music-related body motion. Most of these techniques were developed during my PhD, but have been refined over the course of my post-doc fellowship. The paper is available from the Leonardo web page (or MUSE), and will also be posted in the digital archive at UiO after the 6 month embargo period.