基于双目视觉的聚合积分通道行人检测优化算法  

Optimized Aggregated Channel Features Pedestrian Detection Algorithm Based on Binocular Vision

作　　者：金志刚[1] 赵明昕[1] 张瑞[1] 卫津津[1] Jin Zhigang;Zhao Mingxin;Zhang Rui;Wei Jinjin(School of Electronic Information Engineering,Tianjin University,Tianjin 300072,China)

机构地区：[1]天津大学电子信息工程学院,天津300072

出　　处：《天津大学学报（自然科学与工程技术版）》2016年第12期1225-1230,共6页Journal of Tianjin University：Science and Technology

基　　金：国家自然科学基金资助项目(61201179)

摘　　要：针对单目视觉下复杂室外场景及人体非刚性变化多样时行人检测效率及准确率较低的问题,提出了一种基于双目立体视觉的行人检测方法.首先采用非局部视差聚合匹配方获取稠密视差图;然后以粗细两阶段分割策略获取感兴趣区域,使用最小面积阈值获取潜在行人最小尺度;结合行人最小尺度自适应地减少检测所需数据量,最后基于聚合积分通道模型完成数据建模与行人检测.实验结果表明,该方法能有效利用双目视觉信息解决单目视觉下虚假干扰问题,在实现较高检测精度的同时有效提高检测速度.To solve the problems of low detection rate and accuracy under complex outdoor scene and diverse humannon-rigid changes in monocular vision,a pedestrian detection algorithm based on binocular stereo vision was proposed.First,the dense disparity map was implemented using non-local cost aggregation method.Then the regions ofinterest was acquired by two-stage segmentation method,together with a minimal potential pedestrians scale basedon minimum area threshold.The minimum scale was used to reduce the amount of data required for the pedestriandetection,and finally,the aggregated channel features model was developed for pedestrian training and detection.Experimental results show that the proposed method can improve detection accuracy by efficiently using binocularvision information to avoid false alarms in monocular vision,and accelerate detection rate simultaneously.

关 键 词：行人检测 双目立体视觉 聚合积分通道 区域生长 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]