计算机科学 ›› 2017, Vol. 44 ›› Issue (Z6): 188-191.doi: 10.11896/j.issn.1002-137X.2017.6A.043
邹冲,蔡敦波,刘莹,赵娜,赵彤洲
ZOU Chong, CAI Dun-bo, LIU Ying, Z HAO Na and ZHAO Tong-zhou
摘要： 在基于HOG特征的SVM行人检测算法的基础上,提出了组合分类器的改进算法。该算法首先采用多尺度滑动窗口提取HOG特征,并对单个SVM分别进行训练,再将训练好的SVM分别采用串联、并联结构形成新分类器后对行人进行检测。为解决用多尺度滑动窗口提取特征时产生的目标候选区域重叠问题,采用非极大值抑制算法对重叠区域进行融合,进而得到准确候选区。实验表明,组合的SVM分类器可以有效降低误检率和漏检率。
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