Qualitative Recognition of Motion Using Temporal Texture

Randal C. Nelson and Ramprasad Polana
Department of Computer Science
University of Rochester
Rochester, NY 14627

Abstract: We describe a method of visual motion recognition applicable to a range of naturally occuring motions that are characterized by spatial and temporal uniformity. The underlying motivation is the observation that, for objects that typically move, it is frequently easier to identify them when they are moving than when they are stationary. Specifically, we show that certain statistical spatial and temporal features that can be derived from from approximations to the motion field have invariant properties, and can be used to classify regional activities such as windblown trees, ripples on water, or chaotic fluid flow, that are characterized by complex, non-rigid motion. We refer to the technique as {\em temporal texture analysis} in analogy to the techniques developed to classify gray-scale textures. This {\em recognition} approach contrasts with the {\em reconstructive} approach that has typified most prior work on motion. We demonstrate the technique on a number of real-world image sequences containing complex movement. The work has practical application in monitoring and surveillance, and as a component of a sophisticated visual system.