The conversion of histograms of oriented gradient in different vision-angle and rotation-angle
视角和旋转角变化时梯度方向直方图的转换

In applying the histograms of oriented gradient(HOG) to detect an object, we need a great number of representative image samples to train the classifier. Since the HOG characteristic changes in different vision-angle and different rotation-angle, the detection accuracy will be decreased if images of different vision-angle or rotation-angle are used to train the classifier. By the imaging principle of the camera, we develop an algorithm for converting the HOG characteristic in one vision-angle and rotation-angle to the HOG characteristic in another vision-angle and rotation-angle. Thus, the required number of positive and negative samples for training the classifier is reduced and the classification accuracy of the support-vector-machines(SVM) is raised, eventually resulting in an increase in the object detection accuracy and robustness. Many object-detection experimental results show that this conversion algorithm is effective. This indicates that the proposed algorithm is an efficient tool for HOG-based object detection in practical engineering projects.