Name
Abstract | ||
|---|---|---|
We propose in this paper an original method of camera motion classification based on Transferable Belief Model (TBM). It consists in locating in a video the motions of translation and zoom, and the absence of camera motion (i.e static camera). The classification process is based on a rule-based system that is divided into three stages. From a parametric motion model, the first stage consists in combining data to obtain frame-level belief masses on camera motions. To ensure the temporal coherence of motions, a filtering of belief masses according to TBM is achieved. The second stage carries out a separation between static and dynamic frames. In the third stage, a temporal integration allows the motion to be studied on a set of frames and to preserve only those with significant magnitude and duration. Then, a more detailed description of each motion is given. Experimental results obtained show the effectiveness of the method. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.imavis.2007.01.001 | Image Vision Comput. |
Keywords | DocType | Volume |
fusion architecture,camera motion classification,frame-level belief masse,parametric motion model,original method,motion description,classification process,video indexing,static camera,belief masse,temporal integration,transferable belief model,temporal coherence,camera motion,motion estimation,rule based system | Journal | 25 |
Issue | ISSN | Citations |
11 | Image and Vision Computing | 2 |
PageRank | References | Authors |
0.38 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Guironnet | 1 | 15 | 1.26 |
| Pellerin, D. | 2 | 72 | 7.20 |
| M. Rombaut | 3 | 76 | 6.90 |