CAJ | 학술논문

针对计算机视觉领域的行人检测问题，提出一种基于局部行颜色自相似性特征，该特征可表征为在HSV空间，图像水平方向非重叠对称块颜色直方图的距离信息，结合多层次导向边缘能量特征形成图像的融合特征，利用交叉核支持向量机进行分类。与主流用于行人检测的HOG＋SVM方法相比，其特征维数低，在保证检测精度的同时，大幅提高了算法效率。实验结果验证了该算法的有效性。
침대계산궤시각영역적행인검측문제，제출일충기우국부행안색자상사성특정，해특정가표정위재HSV공간，도상수평방향비중첩대칭괴안색직방도적거리신식，결합다층차도향변연능량특정형성도상적융합특정，이용교차핵지지향량궤진행분류。여주류용우행인검측적HOG＋SVM방법상비，기특정유수저，재보증검측정도적동시，대폭제고료산법효솔。실험결과험증료해산법적유효성。
For pedestrian detection problems in computer vision, this paper proposes a feature based on the local row color self-similarity. In HSV space, this feature represents the color histogram distance of the symmetric non-overlapping blocks in the horizontal direction. It combined Multi-Level Oriented Edge Energy Features with this feature to obtain fusional features, and used Histogram Intersection Kernel Support Vector Machine to classify. Compared to the method of mainstream HOG+SVM, the dimen-sion of this feature is lower. While guaranteeing the detection accuracy, the efficiency of this method is improved mostly. Experiment results validate the effectiveness of the proposed approach.