Name
Abstract | ||
|---|---|---|
This paper presents a specific algorithm for foreground object extraction in complex scenes where the background varies unpredictably over time. The background and foreground models are first constructed by using an adaptive mixture of Gaussians in a joint spatio-color feature space. A dynamic decision framework, which is able to take advantages of the spatial coherency of object, is then introduced for classifying background/foreground pixels. The proposed method was tested on a dataset coming from a real surveillance system including different sensors installed on board a moving train. The experimental results show that the proposed algorithm is robust in the real complex scenarios. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-24085-0_8 | ICIAP (1) |
Keywords | Field | DocType |
foreground pixel,foreground model,adaptive model,complex scene,object detection,specific algorithm,real complex scenario,real surveillance system,fast-varying environment,proposed algorithm,foreground object extraction,classifying background,background subtraction,mixture of gaussians | Background subtraction,Computer vision,Object detection,Feature vector,Pattern recognition,Computer science,Pixel,Artificial intelligence,Mixture model | Conference |
Volume | ISSN | Citations |
6978 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dung Nghi Truong Cong | 1 | 53 | 4.14 |
| Louahdi Khoudour | 2 | 117 | 14.20 |
| Catherine Achard | 3 | 158 | 19.60 |
| Amaury Flancquart | 4 | 8 | 2.72 |