Motion estimation for highly dynamic phenomena such as smoke is an open challenge for Computer Vision. Traditional dense motion estimation algorithms have difficulties with non-rigid and large motions, both of which are frequently observed in smoke motion. We propose an algorithm for dense motion estimation of smoke. Our algorithm is robust, fast, and has better performance over different types of smoke compared to other dense motion estimation algorithms, including state of the art and neural network approaches. The key to our contribution is to use skelet al flow, without explicit point matching, to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this paper we describe our algorithm in greater detail, and provide experimental evidence to support our claims.
@cite_29: The aim of this study is to perform a short term forecasting of dynamic radar clutter evolution (shape and position). This dynamic clutter, like thunderstorms, can be tracked by means of adapted algorithms based on the matching of the morphological skeleton polygonal approximation by relaxation labeling processes. The efficiency of our methods is demonstrated on meteorological radar images. The objective of this application is dedicated to civil traffic regulation according to severe atmospheric phenomenon, as described in Monnier & al. (1997). Through radar environment assessment, we observe radar clutter, like precipitation, submitted to very complex deformations that cannot be modelized easily. In Barbaresco (1999), we proposed a method based on morphological skeleton deformation to forecast the fluid topological evolution. This method allows management of very complex shapes evolution thanks to polygonal approximation of the skeletons and matching of their closed couple of elements by the relaxation algorithm. The skeleton simplifies shape analysis and deformation but also distinguishes clutter displacement and articulated deformation by skeleton matching from homothetic deformation (inflation and deflation) by medial axis (radius of maximal disks contained in the shape) tracking.
We provide an algorithm that makes general assumptions: No strong physical model is applied, instead, we assume that a skelet al structure can be used to characterise broad appearance and skelet al matching can be used to characterise broad motion. This general idea has been used by @cite_29 for rain cloud tracking with radar image; we differ in the way in which both construct and match skeletons. Noted that the proposed method is much simpler compare to @cite_29 .