Abstract
We propose a dynamic visual learning method that aims to identify people by using sparsely distributed multiple surveillance cameras. In the proposed method, virtual viewpoint images are synthesized by interpolating the sparsely distributed images with a simple 3D shape model of the human head, so that virtual densely distributed multiple images can be obtained. The multiple images generate an initial eigenspace in the initial learning step. In the following additional learning step, other distributed cameras capture additional images that update the eigenspace to improve the recognition performance. The discernment capability for personal identification of the proposed method is demonstrated experimentally.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science |
| Editors | H. Kalviainen, J. Parkkinen, A. Kaarna |
| Pages | 130-140 |
| Number of pages | 11 |
| Volume | 3540 |
| Publication status | Published - 2005 |
| Event | 14th Scandinavian Conference on Image Analysis, SCIA 2005 - Joensuu, Finland Duration: 2005 Jun 19 → 2005 Jun 22 |
Other
| Other | 14th Scandinavian Conference on Image Analysis, SCIA 2005 |
|---|---|
| Country | Finland |
| City | Joensuu |
| Period | 05/6/19 → 05/6/22 |
Fingerprint
ASJC Scopus subject areas
- Computer Science (miscellaneous)
Cite this
Dynamically visual learning for people identification with sparsely distributed cameras. / Tanaka, Hidenori; Kitahara, Itaru; Saito, Hideo; Murase, Hiroshi; Kogure, Kiyoshi; Hagita, Norihiro.
Lecture Notes in Computer Science. ed. / H. Kalviainen; J. Parkkinen; A. Kaarna. Vol. 3540 2005. p. 130-140.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Dynamically visual learning for people identification with sparsely distributed cameras
AU - Tanaka, Hidenori
AU - Kitahara, Itaru
AU - Saito, Hideo
AU - Murase, Hiroshi
AU - Kogure, Kiyoshi
AU - Hagita, Norihiro
PY - 2005
Y1 - 2005
N2 - We propose a dynamic visual learning method that aims to identify people by using sparsely distributed multiple surveillance cameras. In the proposed method, virtual viewpoint images are synthesized by interpolating the sparsely distributed images with a simple 3D shape model of the human head, so that virtual densely distributed multiple images can be obtained. The multiple images generate an initial eigenspace in the initial learning step. In the following additional learning step, other distributed cameras capture additional images that update the eigenspace to improve the recognition performance. The discernment capability for personal identification of the proposed method is demonstrated experimentally.
AB - We propose a dynamic visual learning method that aims to identify people by using sparsely distributed multiple surveillance cameras. In the proposed method, virtual viewpoint images are synthesized by interpolating the sparsely distributed images with a simple 3D shape model of the human head, so that virtual densely distributed multiple images can be obtained. The multiple images generate an initial eigenspace in the initial learning step. In the following additional learning step, other distributed cameras capture additional images that update the eigenspace to improve the recognition performance. The discernment capability for personal identification of the proposed method is demonstrated experimentally.
UR - http://www.scopus.com/inward/record.url?scp=26444570344&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=26444570344&partnerID=8YFLogxK
M3 - Conference contribution
VL - 3540
SP - 130
EP - 140
BT - Lecture Notes in Computer Science
A2 - Kalviainen, H.
A2 - Parkkinen, J.
A2 - Kaarna, A.
ER -