Pedestrian detection for intelligent transportation systems combining AdaBoost algorithm and support vector machine

Pedestrians are the vulnerable participants in transportation system when crashes happen. It is important to detect pedestrian efficiently and accurately in many computer vision applications, such as intelligent transportation systems (ITSs) and safety driving assistant systems (SDASs). This paper proposes a two-stage pedestrian detection method based on machine vision. In the first stage, AdaBoost algorithm and cascading method are adopted to segment pedestrian candidates from image. To confirm whether each candidate is pedestrian or not, a second stage is needed to eliminate some false positives. In this stage, a pedestrian recognizing classifier is trained with support vector machine (SVM). The input features used for SVM training are extracted from both the sample gray images and edge images. Finally, the performance of the proposed pedestrian detection method is tested with real-world data. Results show that the performance is better than conventional single-stage classifier, such as AdaBoost based or SVM based classifier.     

基于场景模型与统计学习的鲁棒行人检测算法

提出一种基于场景模型和统计学习的行人检测算法. 针对训练行人检测器时面临的动态场景的复杂性和行人样本多样性等问题, 通过背景建模, 从场景的背景图像上提取有限的负样本用于训练, 大幅度提高了分类器的检测率, 同时降低了虚警; 提出一种快速弱分类器选择算法, 根据正、负样本特征大小的分布和期望的检测率, 直接求解特征大小的阈值范围, 能够满足在线训练和更新检测器的要求; 提出一种基于正样本错误率的训练算法, 先根据正样本加权错误率选择弱分类器, 快速提高检测率, 在训练结束后调整最终分类器的加权系数, 在保证检测率的同时尽可能降低虚警率. 实验中构建了一个试验视频数据库和行人样本库, 数据库包括雨、雪、阴影、季节变化、摄像机平移、旋转、缩放等情况, 并设计实现了一个实时行人检测系统BMAT (Background modeling and Adaboost training), 实验结果证明了算法的有效性.     

Fast asymmetric learning for cascade face detection

A cascade face detector uses a sequence of node classifiers to distinguish faces from non-faces. This paper presents a new approach to design node classifiers in the cascade detector. Previous methods used machine learning algorithms that simultaneously select features and form ensemble classifiers. We argue that if these two parts are decoupled, we have the freedom to design a classifier that explicitly addresses the difficulties caused by the asymmetric learning goal. There are three contributions in this paper. The first is a categorization of asymmetries in the learning goal, and why they make face detection hard. The second is the Forward Feature Selection (FFS) algorithm and a fast pre- omputing strategy for AdaBoost. FFS and the fast AdaBoost can reduce the training time by approximately 100 and 50 times, in comparison to a naive implementation of the AdaBoost feature selection method. The last contribution is Linear Asymmetric Classifier (LAC), a classifier that explicitly handles the asymmetric learning goal as a well-defined constrained optimization problem. We demonstrated experimentally that LAC results in improved ensemble classifier performance.     

基于Codebook背景建模的视频行人检测

针对视频序列,Codebook背景建模算法能检测出其中的运动物体,但却无法识别行人.而大部分基于支持向量机(SVM)训练的行人分类器,需要通过滑动窗口遍历图像检测行人.为加快行人检测的速度,提出将传统的行人分类器融入到Codebook背景建模算法中,通过背景建模算法为行人检测提供候选区域,减少搜索范围,降低了行人误检率;并根据行人的特点,构建临时块模型定期将满足条件的前景区域更新到背景模型中,解决了Codebook背景建模算法不能应对光照突变的问题.实验结果表明:所提算法能应对光照突变所带来的干扰,实现视频行人实时检测.     

基于AdaBoost特征约减的入侵检测分类方法

提出一种基于AdaBoost的入侵特征约减算法,利用该算法约减入侵特征中的冗余特征,构造Ada-加权和Ada-域值分类器,并与支持向量机分类器进行对比。设计并实现Linux实时入侵检测实验平台,并将特征约减算法和3种分类方法应用于该平台。实验结果表明,由特征约减算法挑选出来的入侵特征集较优,Ada-加权和Ada-域值分类器的分类效果优于支持向量机分类器,且Ada-域值分类器在测试集上的检测性能最佳。     

组合SVM分类器在行人检测中的研究

在基于HOG特征的SVM行人检测算法的基础上,提出了组合分类器的改进算法。该算法首先采用多尺度滑动窗口提取HOG特征,并对单个SVM分别进行训练,再将训练好的SVM分别采用串联、并联结构形成新分类器后对行人进行检测。为解决用多尺度滑动窗口提取特征时产生的目标候选区域重叠问题,采用非极大值抑制算法对重叠区域进行融合,进而得到准确候选区。实验表明,组合的SVM分类器可以有效降低误检率和漏检率。     

基于肤色分割和AdaBoost算法的彩色图像的人脸检测

文章提出了肤色分割和AdaBoost算法结合的人脸检测算法。首先,对彩色图像进行肤色分割,通过人脸肤色的统计特征得到候选人脸区域：然后,基于AdaBoost算法,使用由强分类器组成的级联分类器对候选人脸区域进行扫描,最终得到精确定位的人脸。实验证明,该方法具有肤色检测快速和AdaBoost算法误检率低的优点,可以有效的运用于多姿态、多人脸和复杂背景的情况。     

Associated evolution of a support vector machine-based classifier for pedestrian detection

Support vector machine (SVM) has become a dominant classification technique used in pedestrian detection systems. In such systems, classifiers are used to detect pedestrians in some input frames. The performance of a SVM classifier is mainly influenced by two factors: the selected features and the parameters of the kernel function. These two factors are highly related and therefore, it is desirable that the two factors can be analyzed simultaneously, which are usually not the case in the previous work.In this paper, we propose an evolutionary method to simultaneously optimize the feature set and the parameters for the SVM classifier. Specifically, adaptive genetic operators were designed to be suitable for the feature selection and parameter tuning. The proposed method is used to train a SVM classifier for pedestrian detection. Experiments in real city traffic scenes show that the proposed approach leads to higher detection accuracy and shorter detection time.     

基于3D模型和AdaBoost算法的绝缘子检测

随着电力系统直升飞机巡线的不断发展与应用,对于输电线路关键部件的检测与识别越来越受到图像处理工作者的青睐。提出了一种利用3D模型制作训练样本及Ada Boost算法实现的航拍图像绝缘子自动检测方法。根据绝缘子3D模型图像的空间结构特征,提出了能反映这些结构的Haar矩形特征,从中挑选对绝缘子航拍图像有最好区分的特征构成弱分类器,再组合生成强分类器。使用正负样本图像训练后,由强分类器级联组成了一个多层分类器系统。实验结果表明,该方法有效地提升了绝缘子的识别效果,为后续的故障检测工作提供了良好的铺垫。     

人脸检测级联分类器快速训练算法

目前AdaBoost训练算法已被广泛地应用于人脸检测中级联分类器的构建,而AdaBoost算法训练级联分类器的周期却十分漫长.为了减少训练时间,文中提出了一种基于AdaBoost的改进训练算法.该算法通过对弱分类器的阈值选择进行一趟处理来降低运算时间复杂度,并根据AdaBoost训练迭代中只改变样本权值而不更新样本的特点对特征值和排序结果进行缓存来提高训练算法的性能.实验结果表明,该算法大幅提高了人脸检测分类器训练系统的性能,使得分类器的训练时间缩短了60多倍.由于AdaBoost算法的通用性,该改进算法不仅适用于人脸检测,也适合所有进行权值更新迭代训练的Boosting算法.     

Enhancing network intrusion detection classifiers using supervised adversarial training

The performance of classifiers has a direct impact on the effectiveness of intrusion detection system. Thus, most researchers aim to improve the detection performance of classifiers. However, classifiers can only get limited useful information from the limited number of labeled training samples, which usually affects the generalization of classifiers. In order to enhance the network intrusion detection classifiers, we resort to adversarial training, and a novel supervised learning framework using generative adversarial network for improving the performance of the classifier is proposed in this paper. The generative model in our framework is utilized to continuously generate other complementary labeled samples for adversarial training and assist the classifier for classification, while the classifier in our framework is used to identify different categories. Meanwhile, the loss function is deduced again, and several empirical training strategies are proposed to improve the stabilization of the supervised learning framework. Experimental results prove that the classifier via adversarial training improves the performance indicators of intrusion detection. The proposed framework provides a feasible method to enhance the performance and generalization of the classifier.

     

结合样本选择和AdaBoost的日侧冕状极光检测算法

日侧冕状极光是太阳风与地球磁层相互作用产生的典型电离层踪迹,对其正确分类对研究空间天气活动尤为重要。根据冕状极光的形态特征,提出了一种基于静态图像分类的日侧冕状极光检测算法。首先提取极光样本图像的Gabor特征,利用K均值聚类算法进行基于有监督聚类的训练样本选择,保证训练样本的多样性和代表性。然后引入AdaBoost算法进行特征选择并构建级联分类器实现日侧冕状极光的检测。在北极黄河站采集到的实测极光图像数据库上所做的分类实验结果表明了算法的有效性。     

Localized Generalization Error of Gaussian-based Classifiers and Visualization of Decision Boundaries

In pattern classification problem, one trains a classifier to recognize future unseen samples using a training dataset. Practically, one should not expect the trained classifier could correctly recognize samples dissimilar to the training dataset. Therefore, finding the generalization capability of a classifier for those unseen samples may not help in improving the classifiers accuracy. The localized generalization error model was proposed to bound above the generalization mean square error for those unseen samples similar to the training dataset only. This error model is derived based on the stochastic sensitivity measure(ST-SM)of the classifiers. We present the ST-SMS for various Gaussian based classifiers: radial basis function neural networks and support vector machine in this paper. At the end of this work, we compare the decision boundaries visualization using the training samples yielding the largest sensitivity measures and the one using support vectors in the input space.     

An efficient cascaded method for network intrusion detection based on extreme learning machines

Machine learning techniques are widely used for network intrusion detection (NID). However, it has to face the unbalance of training samples between classes as it is hard to collect samples of some intrusion classes. This would produce false positives for these intrusion classes. Meanwhile, since there are various types of intrusions, classification boundaries between different classes are seriously nonlinear. Due to the huge amount of training data, computational efficiency is also required. This paper therefore proposes an efficient cascaded classifier for NID. This classifier consists of a collection of binary base classifiers which are serially connected. Each base classifier corresponds to a type of intrusion. The order of these base classifiers is automatically determined based on the number of false positives to cope with the unbalance of training samples. Extreme learning machine algorithm, which has low computational cost, is used to train these base classifiers to delineate the nonlinear boundaries between classes. This proposed NID method is evaluated on the KDD99 data set. Experimental results have shown that this proposed method outperforms other state-of-the-art methods including decision tree, back-propagation neural network and support vector machines.     

基于相关性的AdaBoost人脸检测算法

为提高传统AdaBoost算法的集成性能,降低算法复杂度,提出2种基于分类器相关性的AdaBoost算法。在弱分类器的训练过程中,加入Q统计量进行判定。每个弱分类器的权重更新不仅与当前分类器有关,而且需要考虑到前面的若干分类器,以有效降低弱分类器间的相似性,剔除相似特征。仿真结果表明,该算法具有更好的检测率,同时可降低误检率,改进分类器的整体性能。     

一种基于多特征和机器学习的分级行人检测方法

针对单幅图像中的行人检测问题,提出了基于自适应增强算法(Adaboost)和支持向量机(Support vector machine, SVM)的两级检测方法, 应用粗细结合的思想有效提高检测的精度.粗级行人检测器通过提取四方向特征(Four direction features, FDF)和GAB (Gentle Adaboost)级联训练得到,精密级行人检测器用熵梯度直方图(Entropy-histograms of oriented gradients, EHOG)作为特征, 通过支持向量机学习得到.本文提出的EHOG特征考虑到熵, 通过分布的混乱程度描述,具有分辨行人和类似人的物体能力. 实验结果表明,本文提出的EHOG、粗细结合的两级检测方法能准确地检测出复杂背景下不同姿势的直立行人, 检测精度优于以往Adaboost方法.     

Effects of classifier structures and training regimes on integrated segmentation and recognition of handwritten numeral strings

In integrated segmentation and recognition of character strings, the underlying classifier is trained to be resistant to noncharacters. We evaluate the performance of state-of-the-art pattern classifiers of this kind. First, we build a baseline numeral string recognition system with simple but effective presegmentation. The classification scores of the candidate patterns generated by presegmentation are combined to evaluate the segmentation paths and the optimal path is found using the beam search strategy. Three neural classifiers, two discriminative density models, and two support vector classifiers are evaluated. Each classifier has some variations depending on the training strategy: maximum likelihood, discriminative learning both with and without noncharacter samples. The string recognition performances are evaluated on the numeral string images of the NIST special database 19 and the zipcode images of the CEDAR CDROM-1. The results show that noncharacter training is crucial for neural classifiers and support vector classifiers, whereas, for the discriminative density models, the regularization of parameters is important. The string recognition results compare favorably to the best ones reported in the literature though we totally ignored the geometric context. The best results were obtained using a support vector classifier, but the neural classifiers and discriminative density models show better trade-off between accuracy and computational overhead.     

半监督SVM分类算法的交通视频车辆检测方法

针对交通场景运动车辆检测中车辆数目统计准确率不高、自适应性不强等问题,提出了一种基于半监督支持向量机(SVM)分类算法的交通视频车辆检测方法。利用人工标记的少量样本,分别训练2个基于方向梯度直方图(HOG)特征与基于局部二值模式(LBP)特征的不同核函数的SVM分类器;结合半监督算法的思想,构建SVM的半监督分类方法(SEMI-SVM),标记未知样本并加入到原样本库中,该方法支持样本库动态更新,避免了繁重的人工标记样本的工作,提高了自适应性;最后,通过三帧差分法提取运动区域,加载分类器在该区域进行多尺度检测,标记检测出来的运动车辆,统计车辆数目。实验结果表明:该方法在具有一定的自适应性的同时,有较高的车辆检测准确率,即使在复杂交通情况下,对运动车辆依然有很好的检测效果。     

基于小训练样本的AdaBoost人脸检测

AdaBoost算法已被广泛地应用于人脸检测系统中,但往往需要大量的训练样本。针对其训练过程复杂冗长的缺陷,选择研究基于少量训练样本的人脸检测问题。采用协方差特征代替图像统计的直方图进行特征提取。为达到更好的分类效果,应用基于Fisher判别式分析的线性超平面分类器,通过AdaBoost算法构成多层级联分类器进行人脸检测。在小数据库里可以看到,与目前用于多数人脸检测系统的类Haar特征相比,该算法在减少训练样本的同时能获得更好的检测效果。     

改进的快速行人检测方法

行人检测在视频监控以及智能车系统中有着广泛的应用前景,为了能够更有效地检测行人,将人脸检测中级联检测框架引入行人检测中,并对其进行改进,采用Gentle AdaBoost算法进行分类器训练,以提高训练效率,同时在训练前引入了特征预筛选,以减少训练时间和系统开销。实验表明,改进后的方法训练时间短,检测精度高,同时具有较快的检测速度。