Falling snow motion estimation based on a semi-transparent and particle trajectory model

This paper presents a motion estimation method for semitransparent objects with a long-range displacement between frames, i.e., falling snow in video. Previous optical flow based methods have been treated with non-transparent, rigid, and fluid-like moving objects in a short-range displacement. However, they fail to match between frames when moving objects are transparent/homogenoeous color in a long-range displacement. To meet with such objects' properties, a two-step algorithm is proposed from rough to refined motion estimation via an energy minimization. First, rough motion of every snow particles is extracted from video using a novel “time filter” method in order to obtain/update a quasi-stationary background in every 30 fps. Second, using such rough optical flow from the first step, the long-range snowflakes' trajectories are estimated and refined by propagation, linking, pruning, and optimization. Experimental results using real falling snow videos show that the proposed method is more effective than a previous optical flow method. Our proposed method is useful for the analysis of natural environment changes.