Abstract
License plate recognition is very important in an automobile society. However, it is very difficult to do it, because a background and a body color of cars are similar to that of the license plate. Furthermore, the detection of cars in a moving at a very high-speed is difficult to be done. In this paper, we propose a new robust thresholds determination method in the various background by using the real coded genetic algorithm (RGA). By using RGA, the most likely plate colors are decided under various light conditions. First, the average brightness Y values of images are calculated. Next, relationship between the Y value and the most likely plate color thresholds (upper and lower bounds) are obtained by GA to estimate threshold equations by using the recursive least squares (RLS) algorithm. Finally, in order to show the effectiveness of the proposed method, we show simulation examples by using real images, and result rate of detection is 85.0%.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the International Joint Conference on Neural Networks |
| Pages | 1227-1231 |
| Number of pages | 5 |
| Volume | 2 |
| Publication status | Published - 2003 |
| Externally published | Yes |
| Event | International Joint Conference on Neural Networks 2003 - Portland, OR, United States Duration: 2003 Jul 20 → 2003 Jul 24 |
Other
| Other | International Joint Conference on Neural Networks 2003 |
|---|---|
| Country | United States |
| City | Portland, OR |
| Period | 03/7/20 → 03/7/24 |
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ASJC Scopus subject areas
- Software
Cite this
A Design of the Object Detection System Using the RGA. / Yoshimori, Seiki; Mitsukura, Yasue; Fukumi, Minora; Akamatsu, Norio.
Proceedings of the International Joint Conference on Neural Networks. Vol. 2 2003. p. 1227-1231.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A Design of the Object Detection System Using the RGA
AU - Yoshimori, Seiki
AU - Mitsukura, Yasue
AU - Fukumi, Minora
AU - Akamatsu, Norio
PY - 2003
Y1 - 2003
N2 - License plate recognition is very important in an automobile society. However, it is very difficult to do it, because a background and a body color of cars are similar to that of the license plate. Furthermore, the detection of cars in a moving at a very high-speed is difficult to be done. In this paper, we propose a new robust thresholds determination method in the various background by using the real coded genetic algorithm (RGA). By using RGA, the most likely plate colors are decided under various light conditions. First, the average brightness Y values of images are calculated. Next, relationship between the Y value and the most likely plate color thresholds (upper and lower bounds) are obtained by GA to estimate threshold equations by using the recursive least squares (RLS) algorithm. Finally, in order to show the effectiveness of the proposed method, we show simulation examples by using real images, and result rate of detection is 85.0%.
AB - License plate recognition is very important in an automobile society. However, it is very difficult to do it, because a background and a body color of cars are similar to that of the license plate. Furthermore, the detection of cars in a moving at a very high-speed is difficult to be done. In this paper, we propose a new robust thresholds determination method in the various background by using the real coded genetic algorithm (RGA). By using RGA, the most likely plate colors are decided under various light conditions. First, the average brightness Y values of images are calculated. Next, relationship between the Y value and the most likely plate color thresholds (upper and lower bounds) are obtained by GA to estimate threshold equations by using the recursive least squares (RLS) algorithm. Finally, in order to show the effectiveness of the proposed method, we show simulation examples by using real images, and result rate of detection is 85.0%.
UR - http://www.scopus.com/inward/record.url?scp=0141795441&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0141795441&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0141795441
VL - 2
SP - 1227
EP - 1231
BT - Proceedings of the International Joint Conference on Neural Networks
ER -