Name
Title | ||
|---|---|---|
Falling snow motion estimation based on a semi-transparent and particle trajectory model |
Abstract | ||
|---|---|---|
This paper presents a motion estimation method for semi-transparent 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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICIP.2009.5413658 | Image Processing |
Keywords | Field | DocType |
filtering theory,image sequences,minimisation,motion estimation,energy minimization,falling snow motion estimation,fluid-like moving objects,long-range displacement,nontransparent moving objects,particle trajectory model,quasistationary background,rigid moving objects,rough optical flow,semitransparent model,semitransparent objects,time filter,two-step algorithm,energy minimization,optical flow,particle trajectories,snow,time filter,transparency | Computer vision,Computer science,Snowflake,Optical filter,Particle trajectory,Minimisation (psychology),Artificial intelligence,Motion estimation,Optical flow,Snow,Energy minimization | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4244-5655-0 | 978-1-4244-5655-0 | 1 |
PageRank | References | Authors |
0.34 | 20 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hidetomo Sakaino | 1 | 1 | 0.34 |
| Yang Shen | 2 | 24 | 3.68 |
| Yuanhang Pang | 3 | 1 | 0.34 |
| Lizhuang Ma | 4 | 498 | 100.70 |