基于改进的加速鲁棒特征的目标识别

龙思源; 张葆; 宋策; 孙保基

doi:10.3788/CO.20171006.0719

Volume 10 Issue 6

Dec. 2017

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Article Contents

Chinese Optics > 2017 > 10(6): 719-725.

LONG Si-yuan, ZHANG Bao, SONG Ce, SUN Bao-ji. Object detection based on improved speeded-up robust features[J]. Chinese Optics, 2017, 10(6): 719-725. doi: 10.3788/CO.20171006.0719

Citation:

LONG Si-yuan, ZHANG Bao, SONG Ce, SUN Bao-ji. Object detection based on improved speeded-up robust features[J]. Chinese Optics, 2017, 10(6): 719-725. doi: 10.3788/CO.20171006.0719

LONG Si-yuan, ZHANG Bao, SONG Ce, SUN Bao-ji. Object detection based on improved speeded-up robust features[J]. Chinese Optics, 2017, 10(6): 719-725. doi: 10.3788/CO.20171006.0719

Citation:

LONG Si-yuan, ZHANG Bao, SONG Ce, SUN Bao-ji. Object detection based on improved speeded-up robust features[J]. Chinese Optics, 2017, 10(6): 719-725. doi: 10.3788/CO.20171006.0719

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Object detection based on improved speeded-up robust features

doi: 10.3788/CO.20171006.0719

LONG Si-yuan^{1, 2
,},
ZHANG Bao^{1
,
,},
SONG Ce¹,
SUN Bao-ji^{1, 2}

1.
Key Laboratory of Airborne Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China

Funds:

Major Innovation Project of CIOMP, CAS, China Y3CX1SS14C

More Information

Corresponding author: ZHANG Bao, E-mail:cleresky@vip.sina.com
Received Date: 11 May 2017
Rev Recd Date: 13 Jul 2017
Publish Date: 01 Dec 2017

Abstract

Abstract

To improve the real-time performance and the accuracy of the SURF algorithm, an algorithm combined with AGAST corner detector and improved SURF feature descriptor is proposed. Firstly, feature points are detected by using AGAST corner detection template. Secondly, the Haar wavelet response with increased diagonal information is used to generate descriptor of feature points. Then, the generated descriptor is encoded by the feature bag and a new feature vector is generated. At last, the classification is fulfilled by Support Vector Machine(SVM). Finally, SVM is used to classify the feature vectors to complete the detection. Detection experiments for different view-points, illumination and scales are conducted respectively using SIFT and SURF algorithm as a control. The results show that the average detection rate of this algorithm is 98.0%, 96.9% and 97.1%, and the average time is 66.1 ms, 79.3 ms and 41.0 ms, respectively, which is better than that of SURF algorithm, and the time consumption is about 1/3 of the SURF algorithm.
- image processing,
- object detection,
- Speeded-Up Robust Features,
- AGAST corner detection

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References(15)

References

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