Motion characterization using optical flow and fractal complexity

Abstract

We developed a technique, using fractal complexity analysis of optical flow in two-dimensional (2-D) videos, to characterize information content in observed motion. Several lines of evidence demonstrate that visually available properties of motion can characterize the state of a system. This paper will describe the method used and will present a test case regarding the accuracy of the method. An analytical comparison of simple human movement (arranging items on a table) and American Sign Language (ASL) will be given as an example application. The normalized spectral density in the range of 0.1 to 15 Hz indicated significantly higher fractal complexity in the optical flow of ASL video data, indicating that information content in 2-D video data can be characterized using complexity analysis of optical flow. The technique used for quantification of information content in visual motion data is likely to be applicable for distinguishing biological versus nonbiological motion in 2-D video data, making inferences about the states of biological objects from the dynamics of optical flow, and in assessing likelihood of information content in a video stream.