计算机科学 ›› 2021, Vol. 48 ›› Issue (11): 219-225.doi: 10.11896/jsjkx.201100174
原晓佩, 陈小锋, 廉明
YUAN Xiao-pei, CHEN Xiao-feng, LIAN Ming
摘要： 针对目标检测时Haar-like特征值过多、计算时间长、无法描述目标纹理特征且识别率一般的问题,提出一种基于滑窗原点信息的阈值自调节IHL(Improved Haar-like LBP)特征提取算法。该算法首先构造了IHL特征编码方法,将Haar-like特征和局部二值LBP特征融合;然后在计算Haar-like型局部二值化特征时,使用高斯矩阵获得符合像素分布规律的自调节阈值;同时在求特征值时引入中心点像素信息,确保提取的特征值的合理性;最后使用AdaBoost训练得到级联分类器,将其载入系统,并在KITTI车辆数据集和INRIA Person行人数据集上进行实验测试。实验结果表明,该方法可在65 s内识别1 102个行人目标,在114.3 s内识别1 852个车辆目标,相比传统算法其可以明显加快目标识别的速度,对行人和车辆目标的识别率均可达到94%以上,其检测准确性相比其他方法也有显著提升。
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