Name
Abstract | ||
|---|---|---|
Variants of the background subtraction method are broadly used for the detection of moving objects in video sequences in different applications. In this work we propose a new approach to the background subtraction method which operates in the colour space and manages the colour information in the segmentation process to detect and eliminate noise. This new method is combined with blob-level knowledge associated with different types of blobs that may appear in the foreground. The idea is to process each pixel differently according to the category to which it belongs: real moving objects, shadows, ghosts, reflections, fluctuation or background noise. Thus, the foreground resulting from processing each image frame is refined selectively, applying at each instant the appropriate operator according to the type of noise blob we wish to eliminate. The approach proposed is adaptive, because it allows both the background model and threshold model to be updated. On the one hand, the results obtained confirm the robustness of the method proposed in a wide range of different sequences and, on the other hand, these results underline the importance of handling three colour components in the segmentation process rather than just the one grey-level component. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.patrec.2007.10.007 | Pattern Recognition Letters |
Keywords | Field | DocType |
colour component,background subtraction method,blob-level knowledge,background subtraction,permanence memory,background noise,colour information,ghost detection,background model,colour space,different application,segmentation process,new method,reflection detection,different sequence,new video segmentation method,shadow detection,threshold model | Background subtraction,Computer vision,Background noise,Color space,Pattern recognition,Segmentation,Image processing,Robustness (computer science),Image segmentation,Pixel,Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
29 | 3 | Pattern Recognition Letters |
Citations | PageRank | References |
30 | 1.41 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enrique J. Carmona | 1 | 266 | 14.01 |
| Javier Martínez-Cantos | 2 | 82 | 4.17 |
| José Mira | 3 | 124 | 9.22 |