Causal Flow

Abstract

Optical flow is a widely used technique for extracting flow information from video images. While it is useful for estimating temporary movement in video images, it only captures one aspect of extracting dominant flow information from a sequence of video images. In this paper, we propose a novel flow extraction approach called causal flow, which can estimate the dominant causal relationships among nearby pixels. We assume flows in video images as pixel-to-pixel information transfer, whereas the optical flow measures the relative motion of pixels. Causal flow is based on the Granger causality test, which measures causal influence based on prediction via vector autoregression, and is widely used in economics and brain science. The experimental results demonstrate that causal flow can extract dominant flow information which cannot be obtained by current methods.