A New Car Seat Detection Method

被引：0

作者：

Li Xiaoguang ^{[1
]}

Zhou Changpeng ^{[2
]}

Zhang Ping ^{[1
]}

机构：

[1] Changchun Guanghua Univ, Changchun, Jilin, Peoples R China

[2] Changchun Univ Technol, Changchun, Jilin, Peoples R China

来源：

PROCEEDINGS OF THE 2017 4TH INTERNATIONAL CONFERENCE ON MACHINERY, MATERIALS AND COMPUTER (MACMC 2017) | 2017年 / 150卷

关键词：

car seat; image detection; SIFT;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In order to detect the products of car seat, propose a new car seat detection method in this paper. First, the car seat image is normalized to 256 pixels *256 pixels image. Then extract SIFT (scale invariant feature transform) feature points and match the points. According to the position of two matched points, the matching results are divided into two categories. One is vertical match point; the other is tilt matching point. Compare the number of the two categories. When the vertical match points are more than the tilt matching points, the answer is correct. Otherwise the answer is wrong. Experimental results show that for the detection of three different types of car seats, the detection accuracy is higher than 98%.

引用

页码：643 / 648

页数：6

共 20 条

[1] Allaire S, 2008, P 2008 IEEE COMP SOC
[2] [Anonymous], P IEEE C COMP VIS PA
[3] [Anonymous], 2007, 18 ANN ACM SIAM S DI
[4] [Anonymous], 2004, INT J COMPUTER VISIO
[5] [Anonymous], COMBINING BELIEF FUN
[6] Janoch A, 2011, 2011 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCV WORKSHOPS)
[7] Lazebnik S., 2005, PAMI
[8] A comprehensive review of current local features for computer vision
Li, Jing
Allinson, Nigel M.
[J]. NEUROCOMPUTING, 2008, 71 (10-12) : 1771 - 1787
[9] Liu RA, 2013, IEEE INT CONF COMM, P946, DOI 10.1109/ICCW.2013.6649371
[10] Liu Zhi-wen, 2013, Optics and Precision Engineering, V21, P2146, DOI 10.3788/OPE.20132108.2146

← 1 2 →