视角和旋转角变化时梯度方向直方图的转换

The conversion of histograms of oriented gradient in different vision-angle and rotation-angle

中文关键词  目标检测  视角  旋转角  梯度方向直方图HOG  支持向量机

英文关键词  object-detection  vision-angle  rotation-angle  histograms of oriented gradient  SVM

中文摘要

      使用梯度方向直方图(HOG)来检测目标, 需要大量的, 有代表性的样本来训练分类器. 一个目标的HOG, 其特征在不同的摄像机视角和不同的光轴旋转角下, 并不相同. 因此, 使用不同视角下的混合样本集来训练分类器时,目标检测的准确率受到样本噪声的影响将会降低. 基于摄像机成像的基本原理, 提出了一种转换算法, 可以把一个样本在某个视角下的HOG特征转换成另一个视角下的HOG特征. 这样既降低了分类器训练时需要采集的正负样本数量, 又提高了支持向量机(SVM)分类的准确性, 从而提高了目标检测的准确性. 大量目标检测实验结果表明本文提出的算法是有效的.

英文摘要

      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.