基于超像素分割的实时野外场景理解  

Real-Time Nature Scene Understanding Based on Superpixel Segmentation

作　　者：高玉潼 原玥[2] GAO Yutong;YUAN Yue(School of Computer Science & Engineering, Northeastern University, Shenyang 110169, China;School of Information Engineering, Shenyang University, Shenyang 110044, China)

机构地区：[1]东北大学计算机科学与工程学院,辽宁沈阳110169 [2]沈阳大学信息工程学院,辽宁沈阳110044

出　　处：《沈阳大学学报（自然科学版）》2019年第3期210-216,共7页Journal of Shenyang University：Natural Science

基　　金：国家自然科学基金资助项目(61401081)

摘　　要：提出了一个实时野外场景理解算法,可以广泛地应用于自主野外环境探索、辅助驾驶系统等方面.为了提升分类算法的速度和精度,采用快速的超像素分割来对场景进行预处理,然后针对每个超像素区域提取HSV颜色特征、LBP纹理特征和EOH边缘梯度特征构建其多维特征向量,并采用多类Real AdaBoost算法进行特征训练得到场景分类器.实验证明,提出的算法不仅具有良好的实时性,同时由于采用超像素进行分割预处理,有效地提升了对场景中不同类别的分类精度.A real-time algorithm for nature scene understanding is proposed, which is able to be used for environment exploration and driving assistance. To improve accuracy and speed, preprocessing is implemented by rapid superpixel segmentation. Combining HSV feature, LBP texture feature and EOH feature to construct feature description, and classifier can be acquired by multi-class Real AdaBoost training. Experimental results prove that the algorithm can meet real time running. Further, classification accuracy is effectively boosted by superpixel segmentation.

关 键 词：超像素分割 HSV颜色特征 LBP特征 EOH特征 多类Real ADABOOST 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]