
As part of another project, I started created “heatmaps” of the motion intensity into video recordings of everyday events. These are not images of literal heat, but assessments of the amount of visual change across the video field, converted into a coloured scale, where “heat” (from blue to red) is a readily-understood representation. My main motivation was to assess where and to identify what attracts attention, or distracts from attention, and to express how the environment feels from an autistic, attention-deficit (ADD/ADHD) perspective. These heatmaps of the amount and location of visual change became quite informative maps of how people use space, and how design constrains people from using space effectively.
(A minimal, fully-functional code sample is appended to the end of this post. You will need Python, and the OpenCV and Numpy libraries installed.)
Continue reading Mapping shared public space using motion intensity