基于数字图像处理的交通信息检测

本文提出了一种基于数字图像处理的交通流量参数的检测方法，通过CMOS传感摄像头对交通道路摄取的车辆和交通场景来有效地检测交通参数，其中主要用到基于DSP的数字图像处理方法来对摄像头获取的图像进行获取、分析、分割及特征提取等。本方法可以完成单车道的城市交通要道的车辆计数和车辆速度检测等交通信息。该方法具有良好的实用性和可靠性，可用于现场采集交通信息以便调控交通道路状况。     

A Parallel Pipeline Based Multiprocessor System For Real-Time Measurement of Road Traffic Parameters

Real-time measurement and analysis of road traffic flow parameters such as volume, speed and queue are increasingly required for traffic control and management. Image processing is considered as an attractive and flexible technique for automatic analysis of road traffic scenes for the measurement and data collection of road traffic parameters. In this paper, the authors describe a novel image processing based approach for analysis of road traffic scenes. Combined background differencing and edge detection techniques are used to detect vehicles and measure various traffic parameters such as vehicle count and the queue length. A RISC based multiprocessor system was designed to enable real-time execution of the authors algorithm. The multiprocessor system has nine processing modules connected in a parallel pipeline fashion. Results shows that the authors multiprocessor system is able to provide measurement of traffic parameters in real-time. Results are presented for real tests of our system by analysing traffic scenes on the highways of Singapore.     

Velocity calculation by automatic camera calibration based on homogenous fog weather condition

A novel algorithm for vehicle average velocity detection through automatic and dynamic camera calibration based on dark channel in homogenous fog weather condition is presented in this paper. Camera fixed in the middle of the road should be calibrated in homogenous fog weather condition, and can be used in any weather condition. Unlike other researches in velocity calculation area, our traffic model only includes road plane and vehicles in motion. Painted lines in scene image are neglected because sometimes there are no traffic lanes, especially in un-structured traffic scene. Once calibrated, scene distance will be got and can be used to calculate vehicles average velocity. Three major steps are included in our algorithm. Firstly, current video frame is recognized to discriminate current weather condition based on area search method (ASM). If it is homogenous fog, average pixel value from top to bottom in the selected area will change in the form of edge spread function (ESF). Secondly, traffic road surface plane will be found by generating activity map created by calculating the expected value of the absolute intensity difference between two adjacent frames. Finally, scene transmission image is got by dark channel prior theory, camera’s intrinsic and extrinsic parameters are calculated based on the parameter calibration formula deduced from monocular model and scene transmission image. In this step, several key points with particular transmission value for generating necessary calculation equations on road surface are selected to calibrate the camera. Vehicles’ pixel coordinates are transformed to camera coordinates. Distance between vehicles and the camera will be calculated, and then average velocity for each vehicle is got. At the end of this paper, calibration results and vehicles velocity data for nine vehicles in different weather conditions are given. Comparison with other algorithms verifies the effectiveness of our algorithm.     

Adaptive Road Recognition and Ego-state Tracking in the Presence of Obstacles

An approach to road recognition and ego-state tracking in monocular image sequences of traffic scenes is described. The main contribution of this paper is the adaptive recognition scheme, which deals with competitive road hypotheses, and its application in several processing steps of an image sequence analysis system. No manual initialization of the tracked road is required and the change of the road type is allowed. The road parameters to be recognized are the road width, road lane number and road curvature. For exact estimation of road curvature the translational and rotational velocities of the ego-car are assumed to be available. The estimated ego-state parameters are the camera orientation (which is derived due to vanishing point tracking) and the camera position relative to the road center line.     

面向智慧交通的图像处理与边缘计算

随着全球人口的持续增长和城市化进程的加速,道路拥挤、交通事故和污染排放增加等问题日益严重。智慧交通系统旨在借助先进的信息与通信技术建成高效安全、环保舒适的交通与运输体系,提供全方位的交通信息服务和安全高效、经济快捷的交通运输与出行服务。经过各国多年来的竭力推进与发展,智慧交通系统在交通管理、自动驾驶与车路协同等方向均得到广泛的应用。智慧交通的发展离不开通信、计算机与控制等研究方向的突破与创新。其中,图像处理作为智慧交通系统的核心技术之一,它的研究进展直接影响着智慧交通系统的部署。图像处理技术是指计算机对图像进行增强、复原、提取特征、分类和分割等技术处理,通过对交通视觉图像的处理,为智慧交通系统的感知、识别、检测、跟踪和路径规划等功能提供了最直接与重要的信息。此外,面对智慧交通系统所产生的大量数据计算任务,边缘计算技术则将中心云服务下沉至各边缘节点附近,不但能够优化算力负载分配,还能够满足智慧交通应用与服务对低时延、高响应速度的需求。本文从智慧交通系统的发展现状入手,分别围绕面向智慧交通的图像处理与边缘计算技术,阐述其研究热点与前沿进展,汇总与比较国内外的相关学术和产业成果,并对智慧交通系统中的图像处理及边缘计算技术未来的发展进行总结分析与趋势展望。     

基于视频技术的交通违章处理系统的设计与实现

针对交通场景中经常出现的车辆违章行为,如闯红灯、违章停车、超速、强行右转弯等,开发研制了基于计算机视觉和图像识别技术的视频交通违章处理系统;该系统在完成初始设定之后,便可以自动地、实时地检测出车辆的违章行为并提供车辆违章的相关信息;文中详细介绍了系统硬件和软件的设计方案以及系统功能的实现机理;利用VC＋＋6.0开发平台,研发了具有从交通场景的视频数据采集、违章行为识别、违章车辆全景图像存储,直至对违章车辆的数据库管理等多个功能的交通违章处理系统;经测试,系统运行稳定、可靠.     

An image detection technique based on morphological edge detection and background differencing for real-time traffic analysis

The real-time vehicle detection from a traffic scene is the major process in image processing based traffic data collection and analysis techniques. The most common algorithm used for real-time vehicle detection is based on background differencing and thresholding operations. The efficiency of this method of image detection is heavily dependent on the background updating and threshold selection techniques. In this paper, a new background updating and a dynamic threshold selection technique is presented. An alternative image detection technique used in image processing is based on edge detection techniques. However, an edge detector extracts the edges of the objects of a scene irrespective of whether it belongs to the background details or the objects. Therefore, to separate these two, extra information is required. We have developed a new image detection method based on background differencing and edge detection techniques, which separates the objects from their backgrounds and works well under various lighting and weather conditions. This image detection technique together with other techniques for calculating traffic parameters e.g. counting number of vehicles, works in real-time on an 80386-based microcomputer operating at a clock speed of 33 MHz.     

基于机器视觉的高速公路车辆停车检测

为减少高速公路上车辆追尾等交通事故的发生,研究了基于机器视觉的车辆停车检测方法.检测系统采用当前帧和背景帧差分的方法实现了检测线圈范围内图像特征提取.分析了车辆从运动到静止的特征提取值图,并利用车辆存在阈值、车辆存在值、系统最小车辆存在值、系统不相似度阈值、系统相似度值、系统相似度最小值、车辆不存在值和系统最小车辆不存在值等参数实现了车辆停车检测.试验表明,该方法能够有效的将停靠在路面上的车辆检测出来.     

Neural Networks for Omni-View Road Image Understanding

This paper presents a new approach to the outdoor roa scene understanding by using omni-view images and backpropagation networks.Both the road directions used for vehicle heading and th road categories used for vehicle localization are determined by the integrated system.There are three main features about the work.First,an omni-view image sensor is used to extract image samples,and the original image is preprocessed so that the inputs of the network is rotation-invariant and simple.Second,the problem of the network size,especially the number of the hidden units,is decided by the analysis of systematic experimental results.Finally,the internal representation,which reveals the properties of the neural network,is analyzed in the view point of visual signal processing.Experimental results with real scene images are encouraging.     

客运车辆运行状态图像监控系统设计

针对公路客运车辆运行中存在的事故多发问题,设计了一种能够实时警示和记录驾驶员违章驾驶行为的客运车辆运行状态图像监控系统。该系统采用SEED-VPM642高速视频图像处理DSP作为图像处理平台,利用CCD图像传感器进行前方道路图像的采集,MC9S12DG128单片机完成车辆行驶状态参数的采集处理及声光预警。实验结果表明,该系统工作可靠性高、价格成本低,具有广泛的应用前景。     

Road traffic density estimation using microscopic and macroscopic parameters

In this paper we present a comparative study of two approaches for road traffic density estimation. The first approach uses the microscopic parameters which are extracted using both motion detection and tracking methods from a video sequence, and the second approach uses the macroscopic parameters which are directly estimated by analyzing the global motion in the video scene. The extracted parameters are applied to three classifiers, the K Nearest Neighbor (KNN) classifier, the LVQ classifier and the SVM classifier, in order to classify the road traffic in three categories: light, medium and heavy. The methods are compared based on their robustness to the classification of different road traffic states. The goal of this study is to propose an algorithm for road traffic density estimation with a high precision.     

基于三维重建的交通流量检测算法

在智能交通系统中 ,道路交通流量信息实时、有效的检测是交通信息系统的关键环节 .固定相机的视频图象检测法具有诸多优点 ,为此 ,提出了一个基于知识的视频图象交通流量检测系统 ,其中 ,车辆的分割和识别是视频检测法的核心 .根据车辆具有较大的运动惯性等运动规律 ,在短时间隔内 ,可以近似认为车辆运动为刚体匀速直线运动 .在这一条件下 ,将刚体上的运动点重投影到道路平面 ,则重投速度与该点的空间位置到路面的高度具有固定的比例关系 .运动特征采用具有较好定位精度的边缘特征 ,并拟合为直线进行运动跟踪匹配 .在识别过程中 ,先假定车辆的模型及其高度 ,然后再根据重投影速度 ,重建车辆的三维空间结构 ,进行基于知识规则的假设校验 .试验结果表明 ,该方法可以较好地解决车辆视频检测中的遮挡、粘连、阴影等情况     

基于视频图像处理的交通事件检测系统

针对目前公路事件发生后不能及时有效检测与报警、事故处理延迟等不足,研究开发了一种基于视频图像处理的交通事件检测系统。利用计算机视觉与数字图像处理技术,对设置在公路上的摄像头采集的视频图像,进行事件检测算法智能处理,自动采集各种交通参数,检测交通事件并及时报警,可有效地减少交通延误,防止二次事件发生,保障道路安全。与传统交通事件检测系统相比,具有直观方便、费用低等优点,拥有良好的市场需求和实用价值。     

A new multi-level statistical model for variable bit rate MPEG sources over ATM networks and its performance study

In this paper, we propose a new traffic model for MPEG-coded video sequences. The proposed modeling scheme uses scene-based traffic characteristics and considers the correlations between frames of consecutive group of pictures (GOPs). Using a simple scene detection algorithm, scene changes are modeled by a state transition matrix and the number of GOPs of a scene state is modeled by a geometric distribution. Frames of a scene are modeled by the mean I, P, and B frame sizes of each state. For more accurate traffic modeling, the residual bits that represent the difference between the original frame size and the mean frame size of each frame type are compensated by autoregressive processes. The modeling results show that our scene-based model can capture the statistical traffic characteristics of the original video sequences well and estimate the queueing performance with good approximation quality.     

快速交通标志检测预处理方法

为简化场景图处理的计算量,针对场景图复杂的颜色信息,提出了一种快速的交通标志检测预处理方法——颜色标准化。将颜色信息复杂的场景图映射成简单的由8种标准颜色组成的图像,并提取5种与交通标志相关的感兴趣颜色,滤除冗余区域后得到标准颜色的交通标志。该方法大幅度简化了场景图颜色信息的复杂性,节省了RGB-HSI模型转换的计算时间。实验表明,该预处理方法具有很好的鲁棒性,快速准确地实现了交通标志检测。     

Road scene analysis for determination of road traffic density

Road traffic density has always been a concern in large cities around the world, and many approaches were developed to assist in solving congestions related to slow traffic flow. This work proposes a congestion rate estimation approach that relies on real-time video scenes of road traffic, and was implemented and evaluated on eight different hotspots covering 33 different urban roads. The approach relies on road scene morphology for estimation of vehicles average speed along with measuring the overall video scenes randomness acting as a frame texture analysis indicator. Experimental results shows the feasibility of the proposed approach in reliably estimating traffic density and in providing an early warning to drivers on road conditions, thereby mitigating the negative effect of slow traffic flow on their daily lives.