Evaluation of driver drowsiness using respiration analysis by thermal imaging on a driving simulator

In this paper, a new non-intrusive driver drowsiness detection method is introduced based on respiration analysis using facial thermal imaging. Drowsiness is the cause of many driving accidents all over the world. Drivers’ respiration system undergoes significant changes from wakefulness to drowsiness and can be used to detect drowsiness. Current respiration measurement methods are intrusive and uncomfortable making respiration the least measured vital sign during driving. In this paper, a new method is presented based on facial thermal imaging to analyze drivers’ respiration signal non-intrusively. Thirty subjects are tested in a car simulator. They are fully awake at the beginning and experience drowsiness during the tests. The mean and the standard deviation of the respiration rate and the inspiration-to-expiration time ratio are extracted from the subjects’ respiration signal. To detect drowsiness, the Support Vector Machine (SVM) and the K-Nearest Neighbor (KNN) classifiers are used. The Observer Rating of Drowsiness method is used for scoring the drowsiness level and validating the proposed method. The performance and the results of both methods are presented and compared. The results indicate that drowsiness can be detected with the accuracy of 90%, sensitivity of 92%, specificity of 85%, and precision of 91%.

     

基于脉搏波检测的驾驶员睡意预警装置

随着驾驶员人数的不断增加,不文明的驾驶行为也越来越多,其中由于疲劳驾驶所引发的交通事故占据相当大的比例,给人民的生命和财产造成了巨大的损失,因此,对于驾驶员睡意预警装置的技术研究具有非常重要的意义和实用价值。通过对人体脉搏波信号进行分析处理,采用能够反映驾驶员睡意状态的脉搏频率特征信号作为依据,由STC89C52单片机、按键、数码管、光电传感器、时钟模块、滤波电路、集成运放等构成系统,设计了驾驶员睡意预警装置。调试结果显示该装置识别准确率高,数值可靠,能够有效的检测驾驶员的睡意状态,并在睡意状态时发出预警。对比市场同类型产品,该装置具有成本低廉,操作简单,能够实现车载等特点,为驾驶员睡意预警技术的相关研究提供了一定的技术和实验基础。     

Profiling drivers based on driver dependent vehicle driving features

This work addresses the problem of profiling drivers based on their driving features. A purpose-built hardware integrated with a software tool is used to record data from multiple drivers. The recorded data is then profiled using clustering techniques. k-means has been used for clustering and the results are counterchecked with Fuzzy c-means (FCM) and Model Based Clustering (MBC). Based on the results of clustering, a classifier, i.e., an Artificial Neural Network (ANN) is trained to classify a driver during driving in one of the four discovered clusters (profiles). The performance of ANN is compared with that of a Support Vector Machine (SVM). Comparison of the clustering techniques shows that different subsets of the recorded dataset with a diverse combination of attributes provide approximately the same number of profiles, i.e., four. Analysis of features shows that average speed, maximum speed, number of times brakes were applied, and number of times horn was used provide the information regarding drivers’ driving behavior, which is useful for clustering. Both one versus one (SVM) and one versus rest (SVM) method for classification have been applied. Average accuracy and average mean square error achieved in the case of ANN was 84.2 % and 0.05 respectively. Whereas the average performance for SVM was 47 %, the maximum performance was 86 % using RBF kernel. The proposed system can be used in modern vehicles for early warning system, based on drivers’ driving features, to avoid accidents.     

基于多特征融合的眼睛状态检测算法研究

疲劳驾驶检测算法研究对提升交通安全有着重要的意义.目前,已有大量关于疲劳驾驶的文献和成果.在疲劳驾驶检测算法中,眼睛开闭状态的判断起着至关重要的作用.深度级联卷积神经网络用来检测人脸和人脸特征,利用Dlib工具快速提取驾驶员人脸特征.基于眼睛特征计算眼睛宽高比,并将眼睛宽高比、传统人眼特征的人眼虹膜等用于判断眼睛开闭的...     

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.     

A novel hybrid KPCA and SVM with GA model for intrusion detection

A novel support vector machine (SVM) model combining kernel principal component analysis (KPCA) with genetic algorithm (GA) is proposed for intrusion detection. In the proposed model, a multi-layer SVM classifier is adopted to estimate whether the action is an attack, KPCA is used as a preprocessor of SVM to reduce the dimension of feature vectors and shorten training time. In order to reduce the noise caused by feature differences and improve the performance of SVM, an improved kernel function (N-RBF) is proposed by embedding the mean value and the mean square difference values of feature attributes in RBF kernel function. GA is employed to optimize the punishment factor C, kernel parameters σ and the tube size ɛ of SVM. By comparison with other detection algorithms, the experimental results show that the proposed model performs higher predictive accuracy, faster convergence speed and better generalization.     

基于数字图像的汽车驾驶员行驶状态判别

通过车载CCD图像传感器摄取图像,在利用中值滤波、边缘检测等图像处理技术去除噪声和获取道路标线的基础上,建立了摄像机的透视投影模型和汽车驾驶员行驶状态模型,研究了车辆行驶过程中相对道路标线的行驶状态参数,以监控驾驶员行车状况。实验证明,该方法获得的行驶状态参数曲线能有效判别驾驶员的行驶状态,为减少驾驶员人为因素导致的交通事故作了有益的探索。     

A multi-objective artificial immune algorithm for parameter optimization in support vector machine

Support vector machine (SVM) is a classification method based on the structured risk minimization principle. Penalize, C; and kernel, σ parameters of SVM must be carefully selected in establishing an efficient SVM model. These parameters are selected by trial and error or man's experience. Artificial immune system (AIS) can be defined as a soft computing method inspired by theoretical immune system in order to solve science and engineering problems. A multi-objective artificial immune algorithm has been used to optimize the kernel and penalize parameters of SVM in this paper. In training stage of SVM, multiple solutions are found by using multi-objective artificial immune algorithm and then these parameters are evaluated in test stage. The proposed algorithm is applied to fault diagnosis of induction motors and anomaly detection problems and successful results are obtained.     

基于SVM的数据融合方法在DIDS中的应用

考虑到传统SVM解决传统IDS问题的困难,建立基于带概率输出信息的SVM局部信息检测和数据融合、决策分析的分布式入侵检测DIDS模型。该模型尽可能利用局部SVM分类器的优势,充分考虑了各局部SVM的性能差别。通过KDD99数据集对该模型的测试,证明该分布式入侵检测模型可以明显地降低入侵检测的漏报率,提高检测精度。     

基于模拟退火支持向量机的入侵检测系统

为了提高入侵检测系统在小样本集条件下的检测效率,将支持向量机用于网络入侵检测.支持向量机的参数决定了检测效率,然而难以选择合适的参数值,因此提出利用模拟退火算法来优化这些参数,并设计出基于参数优化的支持向量机用于入侵检测.通过对样本数据集中的样本进行实验性检测,并与原始支持向量机入侵检测系统进行比较,结果表明模拟退火支持向量机入侵检测系统检测率高、误报率低,并且缩短了训练时间和检测时间.     

基于支持向量机的高速公路事件检测

提出用支持向量机分类方法研究高速公路事件检测问题。阐述了支持向量机分类算法,根据交通事件对交通流参数的影响规律选择了支持向量机的输入量,用高速公路管理处提供的样本数据进行了仿真研究。仿真实验表明,支持向量机事件检测算法具有检测准确率高、训练时间短、泛化能力好等优点,它为事件检测提供了一种切实可行的新思路。     

Optimal feature selection for support vector machines

Selecting relevant features for support vector machine (SVM) classifiers is important for a variety of reasons such as generalization performance, computational efficiency, and feature interpretability. Traditional SVM approaches to feature selection typically extract features and learn SVM parameters independently. Independently performing these two steps might result in a loss of information related to the classification process. This paper proposes a convex energy-based framework to jointly perform feature selection and SVM parameter learning for linear and non-linear kernels. Experiments on various databases show significant reduction of features used while maintaining classification performance.     

A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy

Two parameters, C and σ, must be carefully predetermined in establishing an efficient support vector machine (SVM) model. Therefore, the purpose of this study is to develop a genetic-based SVM (GA-SVM) model that can automatically determine the optimal parameters, C and σ, of SVM with the highest predictive accuracy and generalization ability simultaneously. This paper pioneered on employing a real-valued genetic algorithm (GA) to optimize the parameters of SVM for predicting bankruptcy. Additionally, the proposed GA-SVM model was tested on the prediction of financial crisis in Taiwan to compare the accuracy of the proposed GA-SVM model with that of other models in multivariate statistics (DA, logit, and probit) and artificial intelligence (NN and SVM). Experimental results show that the GA-SVM model performs the best predictive accuracy, implying that integrating the RGA with traditional SVM model is very successful.     

Enhancing prototype reduction schemes with LVQ3-type algorithms

Various prototype reduction schemes have been reported in the literature. Foremost among these are the prototypes for nearest neighbor (PNN), the vector quantization (VQ), and the support vector machines (SVM) methods. In this paper, we shall show that these schemes can be enhanced by the introduction of a post-processing phase that is related, but not identical to, the LVQ3 process. Although the post-processing with LVQ3 has been reported for the SOM and the basic VQ methods, in this paper, we shall show that an analogous philosophy can be used in conjunction with the SVM and PNN rules. Our essential modification to LVQ3 first entails a partitioning of the respective training sets into two sets called the Placement set and the Optimizing set, which are instrumental in determining the LVQ3 parameters. Such a partitioning is novel to the literature. Our experimental results demonstrate that the proposed enhancement yields the best reported prototype condensation scheme to-date for both artificial data sets, and for samples involving real-life data sets.     

支持向量机和遗传算法的人脸识别方法

支持向量机是一种基于小样本学习的有效工具,作为分类器被认为具有很高的推广性能,无需先验知识。但是参数的选取与支持向量机的识别性能是相关的,核函数参数σ²和惩罚因子C对支持向量机识别性能会产生很大的影响。针对支持向量机在人脸识别问题中的应用,提出了一种基于遗传算法（GA）的参数选择优化方法。利用笔者曾提出的基于小波分解和积分投影的人脸特征提取算法对人脸图像进行特征参数提取,然后利用优化的支持向量机进行识别。实验结果表明,该方法是有效的。     

改进GA-PSO优化SVM的行人检测算法

针对当前对于行人检测的准确率和检测效率的要求越来越高,提出一种GA-PSO算法对于支持向量机（SVM）参数优化的行人检测方法。首先,针对梯度直方图特征描述子的维数高、提取速度慢,使用PCA对其进行降维处理;以SVM算法作为分类器,为避免传统单核支持向量机算法检测率低的情况出现,以组合核函数作为分类器核函数,并设置松弛变量,引进惩罚因子,结合遗传算法（GA）和改进权重系数的粒子群算法（PSO）进行组合系数和参数的优化与选择,根据优化后的参数构成最终的SVM分类器进行行人检测。实验结果表明,与传统SVM检测以及其他优化方法相比,检测率方面都有明显改进,且满足对检测效率的要求。     

基于能量分布和共振峰结构的汉语鼻音检测

该文提出了一种基于能量分布和共振峰结构的汉语鼻音检测方法,该方法首先基于Seneff听觉谱提取了一组描述音段能量分布和共振峰结构的特征参数,然后采用支持向量机模型进行检测和分类,得到候选的鼻音,最后根据音段持续时间、前端韵母能量、高低频能量差、中低频能量比等特征对候选的鼻音进行后处理,去除插入错误,提高鼻音检测的准确率。实验结果表明,干净语音鼻音检测准确率可以达到90.4%,信噪比10dB的语音鼻音检测准确率可达到84.4%以上。     

ECG beat classification using particle swarm optimization and support vector machine

In this paper, we propose a novel ECG arrhythmia classification method using power spectral-based features and support vector machine (SVM) classifier. The method extracts electrocardiogram’s spectral and three timing interval features. Non-parametric power spectral density (PSD) estimation methods are used to extract spectral features. The proposed approach optimizes the relevant parameters of SVM classifier through an intelligent algorithm using particle swarm optimization (PSO). These parameters are: Gaussian radial basis function (GRBF) kernel parameter σ and C penalty parameter of SVM classifier. ECG records from the MIT-BIH arrhythmia database are selected as test data. It is observed that the proposed power spectral-based hybrid particle swarm optimization-support vector machine (SVMPSO) classification method offers significantly improved performance over the SVM which has constant and manually extracted parameter.     

Real-Time Warning System for Driver Drowsiness Detection Using Visual Information

Traffic accidents due to human errors cause many deaths and injuries around the world. To help in reducing this fatality, in this research, a new module for Advanced Driver Assistance System (ADAS) for automatic driver drowsiness detection based on visual information and Artificial Intelligence is presented. The aim of this system is to locate, to track and to analyze the face and the eyes to compute a drowsiness index, working under varying light conditions and in real time. Examples of different images of drivers taken in a real vehicle are shown to validate the algorithm.     

Weather Prediction With Multiclass Support Vector Machines in the Fault Detection of Photovoltaic System

Since the efficiency of photovoltaic (PV) power is closely related to the weather, many PV enterprises install weather instruments to monitor the working state of the PV power system. With the development of the soft measurement technology, the instrumental method seems obsolete and involves high cost. This paper proposes a novel method for predicting the types of weather based on the PV power data and partial meteorological data. By this method, the weather types are deduced by data analysis, instead of weather instrument. A better fault detection is obtained by using the support vector machines (SVM) and comparing the predicted and the actual weather. The model of the weather prediction is established by a direct SVM for training multiclass predictors. Although SVM is suitable for classification, the classified results depend on the type of the kernel, the parameters of the kernel, and the soft margin coefficient, which are difficult to choose. In this paper, these parameters are optimized by particle swarm optimization (PSO) algorithm in anticipation of good prediction results can be achieved. Prediction results show that this method is feasible and effective.