Name
Abstract | ||
|---|---|---|
This paper describes a robust and reliable method of human detection for visual surveillance systems. The merit of this method is to use simple shape parameters of silhouette patterns to classify humans from other moving objects such as butterflies and autonomous factory vehicles. An extra function based on the brightness level transformation is used to extract the precise shape of the silhouette patterns. An approach to overcome the occlusions of humans is also proposed. We tested our method for 2,500 images (1,100 from humans and 1,400 from other moving objects). Our test system detected the humans at the rate of 98% (=1,077/1,100) and judged 92% (=1,283/1,400) of the other moving objects as non-humans |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1109/ICPR.1996.547291 | ICPR |
Keywords | Field | DocType |
brightness,computer vision,feature extraction,object recognition,television applications,automated human detection,brightness level,computer vision,feature extraction,shape parameters,silhouette patterns,visual surveillance system | Object detection,Remotely operated underwater vehicle,Computer vision,Pattern recognition,Computer science,System testing,Silhouette,Feature extraction,Robustness (computer science),Artificial intelligence,Mobile robot,Cognitive neuroscience of visual object recognition | Conference |
Volume | ISSN | ISBN |
3 | 1051-4651 | 0-8186-7282-X |
Citations | PageRank | References |
30 | 6.31 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoshinori Kuno | 1 | 932 | 188.83 |
| T. Watanabe | 2 | 252 | 51.28 |
| Y. Shimosakoda | 3 | 30 | 6.31 |
| Seiichi Nakagawa | 4 | 598 | 104.03 |