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Proceedings Paper

Vehicle detection from high-resolution aerial images based on superpixel and color name features
Author(s): Ziyi Chen; Liujuan Cao; Zang Yu; Yiping Chen; Cheng Wang; Jonathan Li
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Paper Abstract

Automatic vehicle detection from aerial images is emerging due to the strong demand of large-area traffic monitoring. In this paper, we present a novel framework for automatic vehicle detection from the aerial images. Through superpixel segmentation, we first segment the aerial images into homogeneous patches, which consist of the basic units during the detection to improve efficiency. By introducing the sparse representation into our method, powerful classification ability is achieved after the dictionary training. To effectively describe a patch, the Histogram of Oriented Gradient (HOG) is used. We further propose to integrate color information to enrich the feature representation by using the color name feature. The final feature consists of both HOG and color name based histogram, by which we get a strong descriptor of a patch. Experimental results demonstrate the effectiveness and robust performance of the proposed algorithm for vehicle detection from aerial images.

Paper Details

Date Published: 2 March 2016
PDF: 6 pages
Proc. SPIE 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015), 99010M (2 March 2016); doi: 10.1117/12.2234762
Show Author Affiliations
Ziyi Chen, Xiamen Univ. (China)
Liujuan Cao, Xiamen Univ. (China)
Zang Yu, Xiamen Univ. (China)
Yiping Chen, Xiamen Univ. (China)
Cheng Wang, Xiamen Univ. (China)
Jonathan Li, Xiamen Univ. (China)


Published in SPIE Proceedings Vol. 9901:
2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015)
Cheng Wang; Rongrong Ji; Chenglu Wen, Editor(s)

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