Affine-Function Transformation-Based Object Matching for Vehicle Detection from Unmanned Aerial Vehicle Imagery
Affine-Function Transformation-Based Object Matching for Vehicle Detection from Unmanned Aerial Vehicle Imagery
复制标题
基于仿射函数变换的无人机图像车辆检测对象匹配
DOI：
10.3390/rs11141708
复制
发表时间：
2019-07
期刊：
影响因子：
5
通讯作者：
Yan Wanqian
中科院分区：
文献类型：
--
作者：
Cao Shuang;Yu Yongtao;Guan Haiyan;Peng Daifeng;Yan Wanqian
基于遥感图像的车辆检测在交通运输相关应用中发挥着重要作用。然而，车辆的尺度变化、方向变化、光照变化、局部遮挡以及图像质量等，给车辆的准确检测带来了很大的挑战。本文提出了一种基于仿射函数变换的目标匹配框架，用于无人机图像的车辆检测。首先，通过超像素分割策略生成有意义和非冗余的补丁；然后，将基于仿射函数变换的目标匹配框架应用于车辆模板和每个patch进行车辆存在性估计；最后，通过匹配代价阈值、车辆位置估计、多重响应消除等步骤对车辆进行检测和定位。对2个无人机图像数据集的定量评价表明，该方法的平均完备性、正确性、质量和f1测度分别为0.909、0.969、0.883和0.938。对比研究还表明，所提出的方法与Faster R-CNN实现了兼容性能，并且在各种工况下的车辆准确检测方面优于其他八种现有方法。
Vehicle detection from remote sensing images plays a significant role in transportation related applications. However, the scale variations, orientation variations, illumination variations, and partial occlusions of vehicles, as well as the image qualities, bring great challenges for accurate vehicle detection. In this paper, we present an affine-function transformation-based object matching framework for vehicle detection from unmanned aerial vehicle (UAV) images. First, meaningful and non-redundant patches are generated through a superpixel segmentation strategy. Then, the affine-function transformation-based object matching framework is applied to a vehicle template and each of the patches for vehicle existence estimation. Finally, vehicles are detected and located after matching cost thresholding, vehicle location estimation, and multiple response elimination. Quantitative evaluations on two UAV image datasets show that the proposed method achieves an average completeness, correctness, quality, and F1-measure of 0.909, 0.969, 0.883, and 0.938, respectively. Comparative studies also demonstrate that the proposed method achieves compatible performance with the Faster R-CNN and outperforms the other eight existing methods in accurately detecting vehicles of various conditions.
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DOI：
10.1109/icsps.2010.5555636
发表时间：
2010-07
期刊：
2010 2nd International Conference on Signal Processing Systems
影响因子：
--
作者：
Hang Jin;Yanming Feng
通讯作者：
Hang Jin;Yanming Feng
影响因子：
4.8
作者：
Jiaxing Zhang;Chao Tao;Z. Zou
通讯作者：
Jiaxing Zhang;Chao Tao;Z. Zou
DOI：
10.1016/j.jvcir.2015.11.002
发表时间：
2016-01-01
影响因子：
2.6
作者：
Razakarivony, Sebastien;Jurie, Frederic
通讯作者：
Jurie, Frederic
影响因子：
1.1
作者：
Hailing Zhou;Lei Wei;D. Creighton;S. Nahavandi
通讯作者：
Hailing Zhou;Lei Wei;D. Creighton;S. Nahavandi
DOI：
10.1023/b:visi.0000029664.99615.94
发表时间：
2004-11-01
影响因子：
19.5
作者：
Lowe, DG
通讯作者：
Lowe, DG