Name
Abstract | ||
|---|---|---|
This paper proposes a method of clustering video frame pixels for a moving object extraction system. Two cascaded classifiers work cooperatively to firstly classify the pixels into background and non-background cluster and then classify the non-background cluster into four clusters. Besides the moving cluster and shadow cluster, two additional clusters, corresponding to the noisy highlighting pixels and the pixels affected by the camera auto iris function in real environment, are observed and modeled. Experiments on our people counting prototype system demonstrate that it can run smoothly with better performance of moving object extraction in long-term video surveillance of complex scenes. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ACVMOT.2005.93 | Proceedings - IEEE Workshop on Motion and Video Computing, MOTION 2005 |
Keywords | Field | DocType |
pixels classification,object extraction,prototype system,shadow cluster,non-background cluster,video frame pixel,additional cluster,camera auto iris,better performance,long-term video surveillance,object extraction system,iris,lighting,human computer interaction,layout,automation | Shadow,Cluster (physics),Computer vision,Pattern recognition,Computer science,Automation,Video tracking,Classification tree analysis,Pixel,Content-addressable storage,Artificial intelligence,Cluster analysis | Conference |
Volume | Issue | ISSN |
null | null | null |
ISBN | Citations | PageRank |
0-7695-2271-8-2 | 2 | 0.45 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maolin Chen | 1 | 13 | 2.88 |
| Gengyu Ma | 2 | 9 | 6.01 |
| Seok-cheol Kee | 3 | 129 | 13.94 |