不同光照和姿态下的航拍车辆检测方法

为了解决在不同光照和姿态下的航拍车辆检测准确度低的问题，基于Fourier-HOG算法提出了一种航拍车辆检测方法。该方法是基于滑动窗口的检测方法。首先，在处理过程中引入图像预处理，可以将背景区域进行有选择的剔除，大大节省检测时间和降低虚警率；其次，提取航拍图像基于局部敏感直方图的光照不变性特征；然后，再提取旋转不变的Fourier-HOG特征。将此特征在线性支持向量机中对车辆目标与非车辆目标进行分类。在后续处理阶段，引入非极大值抑制来降低误检目标。实验结果表明：所提出的车辆检测方法在谷歌地图数据集上进行测试，其检测准确度较高，且时间消耗低于原始的Fourier-HOG检测方法，该方法是一种较为有效的航拍车辆检测方法。

Vehicle Detection From Aerial Photographing Under Different Illumination and Pose

To solve the problem of low detection accuracy of vehicle detection from aerial photographing under different lighting conditions and different postures, a new method based on the Fourier-HOG algorithm was proposed. This method was based on a sliding-window detection approach. First, image preprocessing, which selectively removed the background region, greatly improved the efficiency of detection and reduced the false alarm rate. Second, illumination invariant features were extracted based on local sensitive histogram and then the rotation invariant Fourier-HOG features were extracted. Finally, from the above features, the vehicle and non-vehicle were discriminated in a linear support vector machine ( SVM) classifier. For post-processing, nonmaximum suppression technique was used to reduce a target multiple-detection. Results of the proposed vehicle detection on the Google Map dataset show that it has a higher degree of detection accuracy and consumes less time than that of the original Fourier-HOG detection method. Therefore, this method is a valid vehicle detection from aerial photographing.