Name
Abstract | ||
|---|---|---|
This paper addresses the problem of background modeling for foreground object detection in complex environments. A Bayesian framework that incorporates spectral, spatial, and temporal features to characterize the background appearance is proposed. Under this framework, the background is represented by the most significant and frequent features, i.e., the principal features, at each pixel. A Bayes decision rule is derived for background and foreground classification based on the statistics of principal features. Principal feature representation for both the static and dynamic background pixels is investigated. A novel learning method is proposed to adapt to both gradual and sudden "once-off" background changes. The convergence of the learning process is analyzed and a formula to select a proper learning rate is derived. Under the proposed framework, a novel algorithm for detecting foreground objects from complex environments is then established. It consists of change detection, change classification, foreground segmentation, and background maintenance. Experiments were conducted on image sequences containing targets of interest in a variety of environments, e.g., offices, public buildings, subway stations, campuses, parking lots, airports, and sidewalks. Good results of foreground detection were obtained. Quantitative evaluation and comparison with the existing method show that the proposed method provides much improved results. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/TIP.2004.836169 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
background appearance,background change,foreground object,foreground object detection,dynamic background pixel,statistical modeling,complex background,foreground classification,complex environment,principal feature,foreground detection,background maintenance,background modeling,change detection,statistical model,feature extraction,image segmentation,statistical analysis,background subtraction,image classification,decision theory,decision rule,indexing terms | Background subtraction,Computer vision,Object detection,Change detection,Pattern recognition,Computer science,Foreground detection,Image segmentation,Feature extraction,Pixel,Artificial intelligence,Contextual image classification | Journal |
Volume | Issue | ISSN |
13 | 11 | 1057-7149 |
Citations | PageRank | References |
460 | 19.29 | 23 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liyuan Li | 1 | 912 | 61.31 |
| Weimin Huang | 2 | 1074 | 96.95 |
| Irene Yu-Hua Gu | 3 | 613 | 35.06 |
| Qi Tian | 4 | 1344 | 75.83 |