Efficient data-driven encoding of scene motion using Eccentricity

TL;DR

This paper introduces a computationally efficient method for representing dynamic scenes using static maps derived from Eccentricity data analysis, enabling real-time motion assessment without storing full image sequences.

Contribution

The novel recursive Eccentricity-based approach efficiently encodes scene motion in static maps, reducing computational load and memory usage compared to traditional methods.

Findings

Maps effectively capture temporal scene information

Method operates recursively with minimal memory

Suitable for real-time video analysis applications

Abstract

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.