Name
Abstract | ||
|---|---|---|
This paper proposes a dynamic conditional random field (DCRF) model for foreground object and moving shadow segmentation in indoor video scenes. Given an image sequence, temporal dependencies of consecutive segmentation fields and spatial dependencies within each segmentation field are unified by a dynamic probabilistic framework based on the conditional random field (CRF). An efficient approximate filtering algorithm is derived for the DCRF model to recursively estimate the segmentation field from the history of observed images. The foreground and shadow segmentation method integrates both intensity and gradient features. Moreover, models of background, shadow, and gradient information are updated adaptively for nonstationary background processes. Experimental results show that the proposed approach can accurately detect moving objects and their cast shadows even in monocular grayscale video sequences. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/TPAMI.2006.25 | IEEE Trans. Pattern Anal. Mach. Intell. |
Keywords | Field | DocType |
dynamic models,video signal processing,consecutive segmentation field,dcrf model,foreground object,image segmentation,indoor video scenes,segmentation field,shadow segmentation,monocular grayscale video sequences,conditional random field,image sequence,image sequences,index terms- conditional random fields,foreground object segmentation,dynamic conditional random field model,object detection,approximate filtering algorithm,shadow segmentation method,filtering theory,cast shadow,dynamic probabilistic framework,dynamic conditional random field,shadow detection.,foreground segmentation,image motion analysis,indexing terms,spatial dependence | Conditional random field,Computer vision,Object detection,Shadow,Random field,Scale-space segmentation,Pattern recognition,Computer science,Segmentation,Segmentation-based object categorization,Image segmentation,Artificial intelligence | Journal |
Volume | Issue | ISSN |
28 | 2 | 0162-8828 |
Citations | PageRank | References |
77 | 2.59 | 22 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Wang | 1 | 948 | 155.42 |
| Kia-Fock Loe | 2 | 180 | 20.88 |
| Jiankang Wu | 3 | 576 | 79.80 |