Joint rotation invariant feature for vehicle detection in aerial images

Dawei Li; Mingtao Li; Jianhua Zheng; Yuanchao Wang

doi:10.1117/12.2281589

21 July 2017 Joint rotation invariant feature for vehicle detection in aerial images

Dawei Li, Mingtao Li, Jianhua Zheng, Yuanchao Wang

Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104200W (2017) https://doi.org/10.1117/12.2281589
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China

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.

Citation Download Citation

Dawei Li, Mingtao Li, Jianhua Zheng, and Yuanchao Wang "Joint rotation invariant feature for vehicle detection in aerial images", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104200W (21 July 2017); https://doi.org/10.1117/12.2281589

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