摘要
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。
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 dimension 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.
出处
《微型电脑应用》
2015年第4期4-7,共4页
Microcomputer Applications
基金
国家科技支撑计划(2013BAH09F01)
上海市科委科技创新行动计划(14511106900)
