Collecting Traffic Video Data Using Portable Poles: Survey, Proposal, and Analysis

Several initiatives have been launched to help prevention of traffic accidents and near-accidents across the European Union. To aid the overall goal of reducing deaths and injuries related to traffic, one must understand the causation of the traffic accidents in order to prevent them. Rather than deploying a person to physically monitor a location, the task is eased by camera equipment installed in existing infrastructure, e.g. poles, and buildings, etc. In rural areas there is however a very limited infrastructure available which complicates the data acquisition. But even if there is infrastructure available in either the rural area or the urban area, this might not serve as an ideal position to capture video data from. In this work, we survey and provide an overview of available and relevant portable poles setups with respect to capturing data in both urban areas and rural areas. The conclusion of the survey shows a lack of a mobile, lightweight, compact, and easy deployable portable pole. We therefore design and develop a new portable pole meeting these requirements. The new proposed portable pole can be deployed by 2 persons in 2 hours in both rural areas as well as urban areas due to its compactness. The deployment and usage of the new portable pole is a complimentary tool, which may improve the camera capturing angle in case existing infrastructure is insufficient. This ultimately improves the traffic monitoring opportunities. Further, the survey of selected portable poles provides an excellent overview and can aid multiple applications within road traffic.

Application Progress of Teaching in the Form of Traffic Short Videos in Nursing Education

With the rapid development of the information technology era,the teaching quality requirements continue to surge,and the mode of education in colleges and universities has also carried out certain innovations.The integration of modern information technology into the teaching process and the combination of medical content has become a new hotspot for reform and innovation of medical education at home and abroad.In this paper,we will describe the application of traffic short videos as the main teaching form in nursing education in domestic and foreign studies,and the role of the application of this teaching form in the improvement of theoretical knowledge and clinical skills of nursing students,as well as the impact on the cultivation of nursing students’professional cognition,communication skills,critical thinking,etc.,with the aim of providing new perspectives for the subsequent nursing education.

Accuracy Assessment and Guidelines for Manual Traffic Counts from Pre-Recorded Video Data

Traffic count is the fundamental data source for transportation planning, management, design, and effectiveness evaluation. Recording traffic flow and counting from the recorded videos are increasingly used due to convenience, high accuracy, and cost-effectiveness. Manual counting from pre-recorded video footage can be prone to inconsistencies and errors, leading to inaccurate counts. Besides, there are no standard guidelines for collecting video data and conducting manual counts from the recorded videos. This paper aims to comprehensively assess the accuracy of manual counts from pre-recorded videos and introduces guidelines for efficiently collecting video data and conducting manual counts by trained individuals. The accuracy assessment of the manual counts was conducted based on repeated counts, and the guidelines were provided from the experience of conducting a traffic survey on forty strip mall access points in Baton Rouge, Louisiana, USA. The percentage of total error, classification error, and interval error were found to be 1.05 percent, 1.08 percent, and 1.29 percent, respectively. Besides, the percent root mean square errors (RMSE) were found to be 1.13 percent, 1.21 percent, and 1.48 percent, respectively. Guidelines were provided for selecting survey sites, instruments and timeframe, fieldwork, and manual counts for an efficient traffic data collection survey.

Variable bit rate video traffic modeling by multiplicative multifractal model

Multiplicative multifractal process could well modal video traffic. The multiplier distributions in the multiplicatire multifractal model for video traffic are investigated and it is found that Gaussian is not suitable for describing the multipliers on the small time scales. A new statistical distribution-symmetric Pareto distribution is introduced. It is applied instead of Gaussian for the multipliers on those scales. Based on that, the algorithm is updated so that symmetric pareto distribution and Gaussian distribution are used to model video traffic but on different time scales. The simulation results demonstrate that the algorithm could model video traffic more accurately.

Video-based measurement and data analysis of traffic flow on urban expressways

A new video-based measurement is proposed to collect and investigate traffic flow parameters. The output of the measurement is velocity-headway distance data pairs. Because density can be directly acquired by the reciprocal of headway distance, the data pairs have the advantage of better simultaneity than those from common detectors. By now, over 33 000 pairs of data have been collected from two road sections in the cities of Shanghai and Zhengzhou. Through analyzing the video files recording traffic movements on urban expressways, the following issues are studied：laws of vehicle velocity changing with headway distance, proportions of di0erent driving behaviors in the traffic system, and characteristics of traffic flow in snowy days. The results show that the real road traffic is very complex, and factors such as location and climate need to be taken into consideration in the formation of traffic flow models.

On use of the alpha stable self-similar stochastic process to model aggregated VBR video traffic

The alpha stable self-similar stochastic process has been proved an effective model for high variable data traffic. A deep insight into some special issues and considerations on use of the process to model aggregated VBR video traffic is made. Different methods to estimate stability parameter a and self-similar parameter H are compared. Processes to generate the linear fractional stable noise （LFSN） and the alpha stable random variables are provided. Model construction and the quantitative comparisons with fractional Brown motion （FBM） and real traffic are also examined. Open problems and future directions are also given with thoughtful discussions.

Nonlinear Dynamic Analysis of MPEG-4 Video Traffic

The main research motive is to analysis and to veiny the inherent nonlinear character of MPEG-4 video. The power spectral density estimation of the video trafiic describes its 1/f^β and periodic characteristics.The priraeipal compohems analysis of the reconstructed space dimension shows only several principal components can be the representation of all dimensions. The correlation dimension analysis proves its fractal characteristic. To accurately compute the largest Lyapunov exponent, the video traffic is divided into many parts.So the largest Lyapunov exponent spectrum is separately calculated using the small data sets method. The largest Lyapunov exponent spectrum shows there exists abundant nonlinear chaos in MPEG-4 video traffic. The conclusion can be made that MPEG-4 video traffic have complex nonlinear be havior and can be characterized by its power spectral density,principal components, correlation dimension and the largest Lyapunov exponent besides its common statistics.

Segment-based traffic smoothing algorithm for VBR video stream

Transmission of variable bit rate (VBR) video, because of the burstiness of VBR video traffic, has high fluctuation in bandwidth requirement. Traffic smoothing algorithm is very efficient in reducing burstiness of the VBR video stream by trans- mitting data in a series of fixed rates. We propose in this paper a novel segment-based bandwidth allocation algorithm which dynamically adjusts the segmentation boundary and changes the transmission rate at the latest possible point so that the video segment will be extended as long as possible and the number of rate changes can be as small as possible while keeping the peak rate low. Simulation results showed that our approach has small bandwidth requirement, high bandwidth utilization and low computation cost.

Statistical Multiplexing of Video Traffic and Data Traffic

This paper studies statistical multiplexing performance by input of video traffic and data traffic. The inputs have different Qos requirements such as loss and delay jitter. By applying a modified FBM model, we present methods to estimate effective bandwidth of the aggregated traffic. Simulations were performed to evaluate effective bandwidth. The comparison between the estimation and the simulation shows that the estimations can give correct data for the effective bandwidths in terms of our interests. The analysis of gain by using priority multiplexing also addresses proper Qos configuration for the inputs in order to achieve positive gains.

Real-time traffic information extraction based on compressed video with interframe motion vector

Extraction of traffic information from image or video sequence is a hot research topic in intelligenttransportation system and computer vision. A real-time traffic information extraction method based on com-pressed video with interframe motion vectors for speed, density and flow detection, has been proposed for ex-traction of traffic information under fixed camera setting and well-defined environment. The motion vectors arefirst separated from the compressed video streams, and then filtered to eliminate incorrect and noisy vectors u-sing the well-defined environmental knowledge. By applying the projective transform and using the filtered mo-tion vectors, speed can be calculated from motion vector statistics, density can be estimated using the motionvector occupancy, and flow can be detected using the combination of speed and density. The embodiment of aprototype system for sky camera traffic monitoring using the MPEG video has been implemented, and experi-mental results proved the effectiveness of the method proposed.

智能行车记录仪图像去雾系统的FPGA设计

雾霾天气下,交通道路能见度低,导致所采集到的视频画面退化、图像信息模糊,同时考虑传统系统处理实时性不高等问题,基于ZYNQ平台设计了一种图像去雾系统,并应用于智能行车记录仪系统中.首先,针对传统暗通道去雾算法在天空区域存在失真等问题,提出了一种分割天空区域的策略来修正图像复原参数;然后,针对计算全局大气光值时,需对整幅图像的像素排序消耗大量资源的问题,利用现场可编程门阵列(FPGA)并行运算的优势,提出一种帧迭代方法优化求取大气光值,同时优化了引导滤波的硬件设计;最后,将双路高清多媒体接口(HDMI)资源中,一路作为视频输入,另一路作为视频处理输出,搭建实时交通图像视频处理试验平台.试验结果表明,系统针对雾霾天气下的交通视频具有较好的去雾效果,尤其是可以解决天空区域去雾的失真问题.在对分辨率为1280像素×720像素的交通视频去雾时,可以达到30帧/s的处理速度,满足实时性要求.

Joint Content Placement and Traffic Management for Cloud Mobile Video Distribution over Software-Defined Networks

To cope with the rapid growth of mobile video, video providers have leveraged cloud technologies to deploy their mobile video service system for more cost-effective and scalable performance. The emergence of Software-Defined Networking(SDN) provides a promising solution to manage the underlying network. In this paper, we introduce an SDN-enabled cloud mobile video distribution architecture and propose a joint video placement, request dispatching and traffic management mechanism to improve user experience and reduce the system operational cost. We use a utility function to capture the two aspects of user experience: the level of satisfaction and average latency, and formulate the joint optimization problem as a mixed integer programming problem. We develop an optimal algorithm based on dual decomposition and prove its optimality. We conduct simulations to evaluate the performance of our algorithm and the results show that our strategy can effectively cut down the total cost and guarantee user experience.

面向智能视频监控的空中交通管制员图像分割

为解决复杂场景下空中交通管制员检测与分割精度低、鲁棒性差的问题,提出一种基于掩码区域卷积神经网络(Mask Region-based Convolutional Neural Networks, Mask R-CNN)的管制员图像分割模型ATC Mask R-CNN(ATC Mask Region-based Convolutional Neural Networks)。首先,构建管制员监控图像数据集(ATC Monitor Image Dataset, AMID)并用于模型训练、测试;其次,在主干网络中引入瓶颈注意力模块(Bottleneck Attention Module, BAM)以增强管制员特征提取,采取改进的柔性非极大值抑制算法(Soft Non-maximum Suppression, Soft-NMS)替代NMS算法进行候选框选取,提高对遮挡目标的检测分割;最后,基于AMID进行管制员图像分割试验。结果显示:ATC Mask R-CNN的精确率、召回率和平均精度分别为96.49%、95.62%和88.84%,表明了该方法的有效性。与Mask R-CNN相比,ATC Mask R-CNN有效降低了复杂场景的不利影响,更适用于管制员工作场景,可以为管制大厅安全管理自动化应用提供技术支撑。

基于FFmpeg多线程编码的智能交通监控系统设计

针对智能交通管理设备本身缺乏安全监管,传统视频监控延迟高、画质低、稳定性差的问题,提出一种基于FFmpeg的多线程编码视频流传输方案。通过FFmpeg调用h264_nvenc编码器,实现宏块行级的GPU多线程加速,降低编码延迟。使用Visual Studio 2019和QT15.5开发基于FFmpeg的音视频处理软件,对多路视频流进行封装、推流,并搭建Nginx流媒体服务器进行分发。通过实验表明,该系统整体的传输延迟低于1 s,且拥有良好的率失真特性,监控画面清晰、稳定性高,实现了对交通管理设备实时稳定的安全监控。

无人机辅助智能交通系统中面向视频多播的资源优化

在智能交通系统(Intelligent Transportation System,ITS)中,实时交通视频可以辅助网联车做出更理智的决策。然而,由于车载传感器位置和数量的限制,车辆无法全面掌握交通环境,不利于行车安全;此外,当多个车辆用户请求相同的视频内容时,传统的单播传输模式存在效率低下的问题。为了解决这些问题,基于非正交多址接入(Non-Orthogonal Multiple Access,NOMA)和可伸缩视频编码(Scalable Video Coding,SVC)技术,提出了一种无人机(Unmanned Aerial Vehicle,UAV)辅助ITS的交通视频多播方案。通过联合优化车辆分组和功率分配策略,最大化车辆接收的长期视频质量。将该优化问题建模为一个马尔可夫决策过程(Markov Decision Process,MDP),并采用Soft Actor-Critic(SAC)算法来求解。大量仿真结果表明,该算法具有很强的探索能力,而且收益性能优于传统Actor-Critic(AC)算法。

基于云技术的高速公路视频监控系统研究与设计

国内道路交通行业发展迅速,高速公路出行需求持续增加,现有高速公路视频监控系统承载巨大的压力,难以实现视频监控系统的全联网和深入应用。因此,该研究通过云平台技术,设计并开发一套基于云平台的高速公路视频监控系统,通过接入各路段监控摄像头资源,并将各路段的监控数据实时上传至云平台,以开展相关业务。以此平台系统为基础,逐渐实现高速公路视频监控资源的统一接入和管理,使高速公路视频监控系统发挥“海量融合、自动识别、应急分析、智能管控”等功能。

面向道路交通事故重建的视频特征参照布设方法研究及应用

目的为克服视频车速鉴定中由于监控相机位置变动造成的特征参照难以有效选取、布设的问题,利用图像单应性关系,提出一种面向道路交通事故重建的视频特征参照布设方法。方法首先,检视道路监控视频,大体确定特征参照布设位置;其次,现场布设特征参照,并利用无人机/相机获取包含特征参照的现场照片;再次,求解单应性矩阵,将现场照片转换为监控视频视角;最后,完成现场照片与监控视频的融合匹配。结果通过在道路交通事故鉴定实例中的应用,实现特征参照在监控视频中的布设,验证了面向道路交通事故重建的视频特征参照布设方法的有效性。结论该方法可满足监控相机位置变动情况下车辆行驶速度的求解及行驶边界的确定等需求,可有效服务于利用视频图像的道路交通事故司法鉴定工作。

雷视一体机在隧道交通流监控的研究与应用

基于雷达信号与视频信息的数据融合技术是近年来智能交通流监控的热点应用。现基于应用雷视一体摄像机在南京市横江大道未开通隧道的大量模拟试验,将试验结果和方法成功地应用到南京市燕子矶过江隧道的实际工程中。研究结果证明,在隧道环境使用雷视一体摄像机,能较好地实现车道级精度的车辆位置捕捉、跨画面接力跟踪、路段重演、车辆轨迹回溯等,并为车辆驾驶行为和事件分析提供更好的支撑能力。最后对应用场景和业务领域的拓展做了进一步的展望。

Modelling vehicular interactions for heterogeneous traffic flow using cellular automata with position preference

This paper proposes and validates a modified cellular automata model for determining interaction rate （i.e. number of car-following/overtaking instances） using traffic flow data measured in the field. The proposed model considers lateral position preference by each vehicle type and introduces a position preference parameter fl in the model which facilitates gradual drifting towards preferred position on road, even if the gap in front is sufficient. Additionally, the model also improves upon the conven- tional model by calculating safe front and back gap dynamically based on speed and deceleration properties of leader and follower vehicles. Sensitivity analysis was carried out to determine the effect of β on vehicular interac- tions and the model was calibrated and validated using interaction rates observed in the field. Paired tests were conducted to determine the determining interaction rates validity of the model in Results of the simulations show that there is a parabolic relationship between area occupancy and interaction rate of different vehicle types. The model performed satisfactorily as the simulated interaction rate between different vehicle types were found to be statistically similar to those observed in field. Also, as expected, the interaction rate between light motor vehicles （LMVs） and heavy motor vehicles （HMVs） were found to be higher than that between LMVs and three wheelers because LMVs and HMVs share the same lane. This could not be done using conventional CA models as lateral movement rules were dictated by only speeds and gaps. So, in conventional models, the vehicles would end up in positions which are not realistic. The position preference parameter introduced in this model motivates vehicles to stay in their preferred positions. This study demonstrates the use of interaction rate as a measure to validate micro- scopic traffic flow models.

Trace-Based Analysis of MPEG-4 AVC Video Streams

MPEG-4 AVC encoded video streams have been analyzed using video traces and statistical features have been extracted, in the context of supporting efficient deployment of networked and multimedia services. The statistical features include the number of scenes composing the video and the sizes of different types of frames, within the overall trace and each scene. Statistical processing has been performed upon the traces and subsequent fitting upon statistical distributions (Pareto and lognormal). Through the construction of a synthetic trace, based upon this analysis, our selections of statistical distribution have been verified. In addition, different types of content, in terms of level of activity (quantified as different scene change ratio) have been considered. Through modelling and fitting, the stability of the main statistical parameters has been verified as well as observations on the dependence of these parameters upon the video activity level.