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Proceedings Paper

Joint rotation invariant feature for vehicle detection in aerial images
Author(s): Dawei Li; Mingtao Li; Jianhua Zheng; Yuanchao Wang
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Paper Abstract

This paper introduces a joint feature of Fourier histograms of oriented gradients (FHOG) and local binary pattern (LBP) for vehicle detection in aerial images. Both of them are rotation invariant, so any rotation angle of vehicle in aerial images can be easily detected. A linear support vector machine (SVM) classifier is then trained over the joint feature vectors for the final vehicle detection. We evaluate our method on a public dataset and compare with some state-of-theart algorithms, the proposed joint feature outperforms them in detecting small targets in complicated backgrounds.

Paper Details

Date Published: 21 July 2017
PDF: 5 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104200W (21 July 2017); doi: 10.1117/12.2281589
Show Author Affiliations
Dawei Li, National Space Science Ctr. (China)
Univ. of Chinese Academy of Sciences (China)
Mingtao Li, National Space Science Ctr. (China)
Univ. of Chinese Academy of Sciences (China)
Jianhua Zheng, National Space Science Ctr. (China)
Univ. of Chinese Academy of Sciences (China)
Yuanchao Wang, National Space Science Ctr. (China)
Univ. of Chinese Academy of Sciences (China)


Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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