Name
Abstract | ||
|---|---|---|
In real-time video surveillance system, background noise and disturbance for the detection of moving objects will have a significant impact. The traditional Gaussian mixture model (GMM) has strong adaptive various complex background ability, but slow convergence speed and vulnerable to illumination change influence. the paper proposes an improved moving target detection algorithm based on Gaussian mixture model which increase the convergence rate of foreground to the background model transformation and introducing the concept of the changing factors, through the three frame differential method solved light mutation problem. The results show that this algorithm can improve the accuracy of the moving object detection, and has good stability and real-time. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1117/12.2030634 | Proceedings of SPIE |
Keywords | Field | DocType |
Gaussian mixture model,moving objects detection,Background updating | Convergence (routing),Background subtraction,Object detection,Computer vision,Model transformation,Background noise,Algorithm,Gaussian,Rate of convergence,Artificial intelligence,Mixture model,Mathematics | Conference |
Volume | Issue | ISSN |
8878 | null | 0277-786X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhihua Wang | 1 | 0 | 0.34 |
| Du Kai | 2 | 0 | 0.34 |
| Xiandong Zhang | 3 | 8 | 2.27 |