Learning visual flows: A Lie algebraic approach

We present a novel method for modeling dynamic visual phenomena, which consists of two key aspects. First, the integral motion of constituent elements in a dynamic scene is captured by a common underlying geometric transform process. Second, a Lie algebraic representation of the transform process is introduced, which maps the transformation group to a vector space, and thus overcomes the difficulties due to the group structure. Consequently, the statistical learning techniques based on vector spaces can be readily applied. Moreover, we discuss the intrinsic connections between the Lie algebra and the Linear dynamical processes, showing that our model induces spatially varying fields that can be estimated from local motions without continuous tracking. Following this, we further develop a statistical framework to robustly learn the flow models from noisy and partially corrupted observations. The proposed methodology is demonstrated on real world phenomenon, inferring common motion patterns from surveillance videos of crowded scenes and satellite data of weather evolution.