3D Pose Estimation by Directly Matching Polyhedral Models to Gray Value Gradients

This contribution addresses the problem of pose estimation and tracking of vehicles in image sequences from traffic scenes recorded by a stationary camera. In a new algorithm, the vehicle pose is estimated by directly matching polyhedral vehicle models to image gradients without an edge segment extraction process. The new approach is significantly more robust than approaches that rely on feature extraction since the new approach exploits more information from the image data. We successfully tracked vehicles that were partially occluded by textured objects, e.g., foliage, where a previous approach based on edge segment extraction failed. Moreover, the new pose estimation approach is also used to determine the orientation and position of the road relative to the camera by matching an intersection model directly to image gradients. Results from various experiments with real world traffic scenes are presented.     

A computer vision system for the detection and classification of vehicles at urban road intersections

The paper presents a real-time vision system to compute traffic parameters by analyzing monocular image sequences coming from pole-mounted video cameras at urban crossroads. The system uses a combination of segmentation and motion information to localize and track moving objects on the road plane, utilizing a robust background updating, and a feature-based tracking method. It is able to describe the path of each detected vehicle, to estimate its speed and to classify it into seven categories. The classification task relies on a model-based matching technique refined by a feature-based one for distinguishing between classes having similar models, like bicycles and motorcycles. The system is flexible with respect to the intersection geometry and the camera position. Experimental results demonstrate robust, real-time vehicle detection, tracking and classification over several hours of videos taken under different illumination conditions. The system is presently under trial in Trento, a 100,000-people town in northern Italy.     

Representation of occurrences for road vehicle traffic

Our 3D-model-based Computer Vision subsystem extracts vehicle trajectories from monocular digitized videos recording road vehicles in inner-city traffic. Steps are documented which import these quantitative geometrical results into a conceptual representation based on a Fuzzy Metric-Temporal Horn Logic (FMTHL, see [K.H. Schäfer, Unscharfe zeitlogische Modellierung von Situationen und Handlungen in Bildfolgenauswertung und Robotik, Dissertation, 1996]). The facts created by this import step can be understood as verb phrases which describe elementary actions of vehicles in image sequences of road traffic scenes. The current contribution suggests a complete conceptual representation of elementary vehicle actions and reports results obtained by an implementation of this approach from real-world traffic videos.     

基于图像处理的交通事故检测及责任判定

为了在发生轻微交通事故时, 快速使事故车辆驶离现场, 保证道路畅通, 提出了一种车辆碰撞检测及责任判定模型. 首先结合SSD目标检测算法(single shot multibox detector)和MobileNet轻量级深度网络模型, 对其进行改进以获取每一帧视频图像中运动目标的位置和大小信息, 实现对车辆识别与检测. 其次, 利用卡尔曼滤波器对连续图像帧之间的运动目标建立对应匹配关系, 预测目标的运动状态, 对目标的位置及运动趋势做出判断, 实现车辆轨迹跟踪. 随后通过车辆目标检测框的交并比判断是否发生碰撞. 最后针对直行道路中车辆的速度、方向信息结合道路安全条例及机动车事故快速方法对事故车辆进行责任判定. 结果分析表明, 该研究可实现直行道路场景下的追尾及变道引发的车辆碰撞检测及责任判定.     

视频图像中的车型识别

文章介绍一种在固定单摄像头拍摄交通图像序列中检测车辆的方法。处理过程大致分为以下三步:重建不含运动目标的自然背景及图像分割;摄像机标定;目标区域的跟踪和车型识别。实验证明方法是可行的。     

基于Radon变换的视频测速算法

为了从视频监测图像中自动提取车辆行驶速度,提出了一种基于Radon变换的视频测速算法。根据车辆行驶轨迹的时空特点,利用道路交通标线为图像与道路建立距离映射关系,简化了现场标定的条件和过程;利用时间堆栈成像方法,建立了车辆行驶轨迹的时空关系;基于Radon变换的图像处理,实现了行驶车辆的视频测速技术。算法具有较好的鲁棒性,与传统方法相比,计算复杂性也要低很多。     

Estimating relative vehicle motions in traffic scenes

Autonomous operation of a vehicle on a road calls for understanding of various events involving the motions of the vehicles in its vicinity. In this paper we show how a moving vehicle which is carrying a camera can estimate the relative motions of nearby vehicles. We show how to “smooth” the motion of the observing vehicle, i.e. to correct the image sequence so that transient motions (primarily rotations) resulting from bumps, etc. are removed and the sequence corresponds more closely to the sequence that would have been collected if the motion had been smooth. We also show how to detect the motions of nearby vehicles relative to the observing vehicle. We present results for several road image sequences which demonstrate the effectiveness of our approach.     

基于改进动态阈值的运动车辆实时快速检测方法*

提出了复杂交通环境下一种新的运动车辆检测方法。基于背景差分获得运动图像,利用自适应阈值选取方法分别对差分图像的三个颜色通道进行二值化,从而实现运动目标的精确检测。根据检测结果,采用中值更新方法实现背景图像的实时更新。实验结果表明,这种基于改进动态阈值和自适应背景相结合的快速检测算法可以从复杂交通场景图像序列中快速有效地检测出运动目标,能够很好地满足智能交通监控系统中运动车辆实时检测的要求。     

基于多特征融合的视频交通数据采集方法

提出了一种基于多特征融合的视频交通数据采集方法, 核心思想是: 在图像中设置虚拟线圈, 假设车辆从虚拟线圈上驶过时引起像素变化, 通过识别这种像素变化来检测车辆并估计车速. 与现有技术相比, 本文的贡献在于: 1) 综合利用虚拟线圈内的前景面积、纹理变化、像素运动等特征来检测车辆, 提出了有效的多特征融合方法, 显著提高了车辆检测精度; 2) 根据单个虚拟线圈内的像素运动向量来估计车速, 避免了双线圈测速法的错误匹配问题. 算法测试结果表明本文算法能够在复杂多样的交通场景和天气条件下, 准确地检测车辆和估计车速. 在算法研究的基础上, 研制了一款嵌入式交通视频检测器, 在路口长期采集交通数据, 为交通信号控制和交通规律分析提供决策依据.     

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.     

Computer vision techniques for traffic flow computation

This paper describes an application of computer vision techniques to road surveillance. It reports on a project undertaken in collaboration with the Research and Innovation group at the Ordnance Survey. The project aims to produce a system that detects and tracks vehicles in real traffic scenes to generate meaningful parameters for use in traffic management. The system has now been implemented using two different approaches: a feature-based approach that detects and groups corner features in a scene into potential vehicle objects, and an appearance-based approach that trains a cascade of classifiers to learn the appearances of vehicles as an arrangement of a set of pre-defined simple Haar features. Potential vehicles detected are then tracked through an image sequence, using the Kalman filter motion tracker. Experimental results of the algorithms are presented in this paper.     

Visual Surveillance for Moving Vehicles

An overview is given of a vision system for locating, recognising and tracking multiple vehicles, using an image sequence taken by a single camera mounted on a moving vehicle. The camera motion is estimated by matching features on the ground plane from one image to the next. Vehicle detection and hypothesis generation are performed using template correlation and a 3D wire frame model of the vehicle is fitted to the image. Once detected and identified, vehicles are tracked using dynamic filtering. A separate batch mode filter obtains the 3D trajectories of nearby vehicles over an extended time. Results are shown for a motorway image sequence.     

Behaviour recognition of ground vehicle using airborne monitoring of unmanned aerial vehicles

This paper proposes a behaviour recognition methodology for ground vehicles moving within road traffic using unmanned aerial vehicles in order to identify suspicious or abnormal behaviour. With the target information acquired by unmanned aerial vehicles and estimated by filtering techniques, ground vehicle behaviour is first classified into representative driving modes, and then a string pattern matching theory is applied to detect suspicious behaviours in the driving mode history. Furthermore, a fuzzy decision-making process is developed to systematically exploit all available information obtained from a complex environment and confirm the characteristic of behaviour, while considering spatiotemporal environment factors as well as several aspects of behaviours. To verify the feasibility and benefits of the proposed approach, numerical simulations on moving ground vehicles are performed using realistic car trajectory data from an off-the-shelf traffic simulation software.     

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.     

Vision-based vehicle detection for a driver assistance system

On the basis of a cost-effective embedded system, this work implements a preceding vehicle detection system by using computer vision technologies. The road scenes are acquired with a monocular camera. The features of the vehicle in front are extracted and recognized by the proposed refined image processing algorithm, and a tracking process based on optical flow is also applied for reducing the complexity of computing. The system also provides the longitudinal distance information for the further function of adaptive cruise control. Moreover, voice alerts and image recording will be activated if the distance is less than the safe range. A statistical base of 100 video road images are tested in our experiments; the natures of the vehicles include sedan, minivan, truck, and bus. The experimental results show that the proportion of correct identifications of proceeding vehicles is above 95.8%, testing on highways in the daytime. Experimental results also indicate that the system correctly identifies vehicles in real time.     

基于概率算法自适应更新背景的运动车辆检测

交通流量检测是智能交通系统中的一个重要研究方向和热点问题,基于视频的车辆检测是交通流量采集分析的核心技术,它为交通流量参数的实时获取提供了可能。为实现在复杂交通视频场景中实时准确检测各类的运动车辆,在研究传统背景差分算法的缺点的工作基础上,提出一个自适应的贝叶斯概率背景检测算法,进而完成了较准确的运动车辆分类检测。实验结果表明该方法具有高效实时的特点,能够较准确地实现复杂交通路面的背景提取和运动车辆的检测,具有良好的鲁棒性。     

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     

基于车型聚类的交通流参数视频检测

单目摄像机成像丢失深度信息,且PTZ (Pan/Tilt/Zoom)摄像视频场景多变,导致交通流参数提取误差较大. 提出了一种基于车型聚类的交通流参数检测方法. 在改进的摄像机自标定成像模型中,提取PTZ 参数变化下的透视投影不变量"伪形状特征', 对其进行基于贡献率算法的车型聚类分析,以车型均高代替实际高度,获取车辆的长宽, 进而计算道路空间占有率,并提升车速检测精度. 测试表明实时性较高,车型聚类自适应于不同场景,平均准确度为96.9%,车长计算精度优于90%.     

Symmetry-based monocular vehicle detection system

In this paper, we describe the development of a symmetry-based vehicle detection system. The system uses a single forward looking camera to capture the road scene. Vehicles are detected based on their edges and symmetrical characteristics. A method to extract the symmetric regions in the image using a multi-sized window and clustering technique is introduced. We hypothesize the vehicle’s locations in the image from the detected symmetric regions and the regions are then further processed to enhance their symmetrical edges. A bounding box of a vehicle is detected from the projection maps of the enhanced vertical and horizontal edges. The hypothesized vehicles are then verified using a two-class classifier, which consists of an edge oriented histogram (EOH) feature extractor and a support vector machine (SVM). Once a vehicle is verified, a tracking process based on a Kalman filter and a reliability point system is used to track the movement of the vehicle in consecutive video frames. The system was successfully implemented and tested on a standard PC. Experimental results on live video feed and pre-recorded video sequences for various road scenes showed that the system is able to detect multiple vehicles in real time.