Name
Abstract | ||
|---|---|---|
Video surveillance is a widely used technology. Moving object detection is the most important content of video surveillance. Background modeling is an important method in moving object detection. However, background modeling algorithm is usually computationally intensive when the size of video is large. Kernel density estimation method based on Chebyshev inequality (KDSBC) is a new background modeling algorithm. This paper present MRKDSBC based on MapReduce which is a distributed programming model. Further more, we prove the correctness of the algorithm theoretically and implement it on Hadoop platform. Finally, we compare it with traditional algorithm. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-31346-2_75 | ISNN (1) |
Keywords | Field | DocType |
background modeling,traditional algorithm,video surveillance,important content,new background modeling algorithm,algorithm theoretically,object detection,kernel density estimation method,background modeling algorithm,important method,chebyshev inequality,kernel density,estimation | Chebyshev's inequality,Object detection,Computer science,Correctness,Algorithm,Theoretical computer science,Artificial intelligence,Machine learning,Kernel density estimation | Conference |
Citations | PageRank | References |
1 | 0.39 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cong Wan | 1 | 3 | 2.16 |
| Cuirong Wang | 2 | 110 | 15.54 |
| Kun Zhang | 3 | 1 | 0.39 |