【作者】 文学志；

【导师】 赵宏；

【作者基本信息】 东北大学 ， 计算机应用技术， 2008， 博士

【摘要】 随着汽车的普及,交通事故已经成为现代社会的第一公害。特别是在国内,交通事故引起的人员伤亡和财产损失的数目更是触目惊心。尤为引人关注的是,在世界范围由于人为因素造成的交通事故占85%左右。而在我国,交通事故更是95%左右都是人为因素造成。为此,利用各种传感器(如视觉传感器和雷达传感器)技术,提高对路面对象的辨识能力,给驾驶人以更多的安全警示及辅助,从而提高车辆的主动安全性,是智能交通系统(Intelligent Transportation System, ITS)领域发展的重要方向。视觉传感器由于成本低、提供的视野信息范围大,因而基于视觉传感器的路面对象识别是ITS领域近些年的研究热点之一。目前,基于视觉的路面对象识别方法大部分都遵循两步法步骤：假设产生和假设验证。假设产生用于得到感兴趣区域(ROI, Region of Interest),假设验证用于对ROI进行对象(车辆和摩托车)存在性验证。机器学习方法是用于假设验证的主要方法之一。由于基于机器学习的假设验证方法比其他假设验证方法具有更大的潜力和更好的应用价值,使得其成为基于视觉的道路对象识别中的研究热点。当前,研究人员提出了许多基于机器学习的道路对象识别方法,本文在对当前机器学习方法在道路对象识别中所存在的以及需要进一步研究的问题进行深入分析的基础上,对机器学习中的特征提取及特征降维、支持向量机(SVM, Support Vector Machine)分类器的参数选择、AdaBoost算法、样本自动化准备及样本不平衡的分类问题进行了系统、深入的研究。首先,针对当前灰度空间基于有符号的Haar小波系数提取的特征用于分类识别时存在着对光照敏感以及抗噪能力弱的问题,提出了一种基于HSV颜色空间的Haar小波特征方法,该方法基于无符号Haar系数来提取对象特征。试验结果表明,该方法不仅提高了算法的识别性能,而且泛化性更好。针对当前的特征提取方法计算量大、耗时长的问题,提出一种类Haar特征提取方法,该方法与当前典型的特征提取方法相比,不仅识别性能好,而且计算速度快。针对当前特征提取算法获取的特征向量其维数过高的问题,提出一种特征降维方法,该方法一方面能降低特征向量的特征维数,从而减少了分类识别时的计算量；另一方面可以去除所提取特征中的冗余信息及噪声信息,提高其在目标物分类识别时的泛化性。其次,针对支持向量机交叉验证选参方法耗时过长的问题,提出了一种改进的支持向量机交叉验证选参方法。该方法不仅能够明显缩短支持向量机的参数选择时间,而且利用所选择的参数训练得到的支持向量机的识别性能接近于传统的交叉验证法得到的支持向量机的识别性能。第三,针对AdaBoost算法存在的分类器准备时间过长的问题,提出了一种改进的AdaBoost弱分类器构造方法,该方法显著减少了分类器的训练准备时间,同时,在弱分类器构造过程中所提出的自适应阈值设置方法较好地克服传统阈值设置方法未能反映训练样本的分布规律的问题。由于AdaBoost算法的分类器训练准备过程本身非常耗时,尤其训练样本规模比较大时。基于此,提出一种增量学习方法,该方法在提高算法的分类性能的同时能够有效提高算法的学习效率。第四,针对当前样本准备对人的依赖性过强的问题,提出一种样本自动化准备方法,该方法能较好地解决样本准备过程中所存在的费时费力以及主观性过强的问题；针对道路对象识别中存在的样本比例严重失衡从而导致分类器识别结果倾向于多类样本的问题,提出一种基于多支持向量机的样本不平衡分类问题解决方法,该方法采用间隔抽样方式来重构训练样本。该方法不仅能有效解决以往方法存在的丢失分类信息过多或因样本量成倍扩大导致训练准备时间增加或因采用随机取样进行分堆所存在不能较好地维持训练样本的分布规律的问题。最后,将所取得的研究成果应用于基于单目视觉的后方对象识别系统中,从不同时间、不同天气条件以及不同背景下的试验结果可以看出：早晨黄昏以及白天恶劣天气(雨、雪、雾)场景下的车辆测试结果验证了本文所取得的研究成果的有效性；白天(晴天、阴天)场景下的车辆及摩托车测试结果验证了本文所取得的研究成果的有效性和可行性。更多还原

【Abstract】 Along with the popularization of vehicles, traffic accidents have become one of the biggest social problems all over the world. Especially in China, the losses, which are caused by traffic accidents in both lives and economy, are startling. It is worth the whistle that about 85% of these accidents are due to human factors worldwide and even 95% in China. Therefore, it has become the key research direction in ITS (Intelligent Transportation System) field to promote the vehicle active security through improving object recognition performance and providing drivers with more alert and assistance information using the techniques of sensors such as video and radar sensors. On-road object recognition based on video sensors has become one of the focuses due to the low cost and the wide vision scene. At present, most methods of road object recognition based on vision follow two steps:hypothesis generation (HG) and hypothesis verification (HV). HG generates ROI (Regions of Interest) which include candidate objects; HV verifies the existence of object on ROI. Machine learning is one of the main methods of HV and has become an important research topic of vision-based road object recognition for its potential and availability. Currently, many machine learning based methods have been proposed for on-road object recognition. After systematically analyzing current problems of machine learning based object recognition, this thesis focuses on the following key techniques of machine learning:feature extraction and reduction, parameter selection of SVM (Support Vector Machine), AdaBoost algorithm, samples automatic preparation and the classification problem caused by imbalanced training samples. These key techniques are studied systematically.Firstly, considering the problem that the current Haar wavelet feature extraction methods based on signed coefficients on the grayscale image are sensitive to the illumination and poor anti-noise, a Haar-like feature extraction method is proposed. Compare to the current typical feature extraction methods, this approach not only has better performance, but can be calculated rapidly. Furthermore, the dimension of feature vector obtained with the current feature extraction methods is usually too high, so a feature reduction approach is proposed. On one hand, this method can reduce the dimension and can avoid the difficulties during classification calculation, on the other hand, it can remove the redundant information and noise of feature vector and improve the generalization of classification.Secondly, because the current parameter selection of RBF-SVM is time consuming, an improved parameter selection method is provided, by which the time consumption can be reduced in the period of parameters selection and training, while the detection performance is closed to that of the traditional cross-validation method.Thirdly, considering the time consumption of AdaBoost classifier preparation, an improved construction method of weak classifier of AdaBoost is proposed, which can reduce time consumption of the training process of AdaBoost classifier, moreover, the proposed self-adaptive threshold setting method can overcome the problem that the traditional threshold setting method can’t reflect the distribution of training samples. Considering the time consuming of the training process of AdaBoost classifier itself, especially the great scale training samples, an increasing learning approach is proposed to effectively improve AdaBoost classifier’s ability and learning efficiency.Fourthly, on the analysis that current sample preparation is strongly dependent to manual operation, an automotive sample preparation method is proposed, which aims to deal with the problems of serious subjectivity and burdensome task when preparing the samples. In addition, since the classifier will bias to multi-class samples when the samples is imbalanced, a SVM ensemble based method is proposed to solve the training of imbalanced samples, this method reconstructs the training samples by using interval sampling method. The proposed method can overcome the problems of losing classification information, time consumption and unstable detection performance caused by random sampling.Finally, the achieved research results are applied to the rear-object (including vehicle and motorcycle) detection system based on monocular vision. The conclusions under different time, different weather and different background conditions are drawn as follows:the test results under twilight and bad weather (including rain, snow and fog) scenes demonstrate the effectiveness of the presented algorithms, and the test results under sunny and cloudy weather at daytime prove the effectiveness and availability.更多还原