摘要
提出一种基于类haar特征和改进AdaBoost分类器的车辆图像识别算法,以解决当前基于SVM分类器或级联分类器存在的分类识别性能不足以及传统基于AdaBoost算法的训练所需时间过长的问题.首先,基于积分图提取图像的扩展类haar特征,然后对所提取的海量类haar特征应用改进的AdaBoost分类器训练方法进行特征选择及分类器训练,最后利用所选择的特征信息及训练得到的分类器进行两类分类识别.实验结果表明,文中方法无论是在识别性能还是训练所需时间方面均明显优于传统方法,具有较好的应用前景.
An algorithm based on Haar-like features and AdaBoost classifier for vehicle recognition is proposed to solve the problem of poor recognition performance based on SVM（Support Vector Machines） classifier and cascaded AdaBoost classifier as well as the problem of much time consumed for training traditional AdaBoost.At first,the extended Haar-like features are extracted using integral image method,then a small number of critical features from a very large set of Haar-like features are selected while training AdaBoost,finally two classes classification is performed using the AdaBoost classifier and the selected features.Experimental results demonstrate that the proposed approaches has better performance both in recognition and time consuming than traditional methods,and shows promising perspective.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2011年第5期1121-1126,共6页
Acta Electronica Sinica
基金
国家863高技术研究发展计划项目(No.2006AA1Z221)
国家自然科学基金(No.60702076)
南京信息工程大学科研基金资助项目(No.20100394)
江苏高校优势学科建设工程资助项目
