  This paper presents a novel approach of representing dynamic visual scenes
with static maps generated from video/image streams. Such representation allows
easy visual assessment of motion in dynamic environments. These maps are 2D
matrices calculated recursively, in a pixel-wise manner, that is based on the
recently introduced concept of Eccentricity data analysis. Eccentricity works
as a metric of a discrepancy between a particular pixel of an image and its
normality model, calculated in terms of mean and variance of past readings of
the same spatial region of the image. While Eccentricity maps carry temporal
information about the scene, actual images do not need to be stored nor
processed in batches. Rather, all the calculations are done recursively, based
on a small amount of statistical information stored in memory, thus resulting
in a very computationally efficient (processor- and memory-wise) method. The
list of potential applications includes video-based activity recognition,
intent recognition, object tracking, video description, and so on.
