Name
Abstract | ||
|---|---|---|
This paper presents a new method for background subtraction which takes advantages of using the color invariants combined with gray color. The proposed method works robustly reducing misclassified foreground objects. Gaussian mixtures are exploited for each pixel through two channels: the color invariants, which are derived from a physical model, and the gray colors obtained as a descriptor of the image. The background models update is performed using a random process selected considering that in many practical situations it is not necessary to update each background pixel model for each new frame. The novel algorithm has been compared to three state-of-the-art methods. Experimental results demonstrate the proposed method achieves a higher robustness, is less sensitive to noise and increases the number of pixel correctly classified as foreground for both indoor and outdoor video sequences. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CCST.2014.6987024 | ICCST |
Keywords | DocType | ISSN |
gaussian processes,image colour analysis,image sequences,mixture models,random processes,video signal processing,gaussian mixtures,background subtraction method,color invariants,gray color,grayscale levels,image descriptor,indoor video sequences,misclassified foreground objects,outdoor video sequences,physical model,background subtraction,video systems,automatic monitoring | Conference | 1071-6572 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| lorena guachi | 1 | 0 | 0.34 |
| Giuseppe Cocorullo | 2 | 106 | 17.00 |
| Pasquale Corsonello | 3 | 278 | 38.06 |
| Fabio Frustaci | 4 | 129 | 17.55 |
| Stefania Perri | 5 | 264 | 33.11 |