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.

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.

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.

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.

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.

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.

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。

基于视频业务的流量激发研究

视频业务深深地渗透到人民的生活中,但网络质量问题会导致视频清晰度不高、观看卡顿,用户体验感知不佳,造成视频业务流量流失。本文研究高清视频感知与5G网络指标的拐点,根据拐点对网络进行优化,提升网络质量,使视频用户更容易获得优质的视频体验,同时因为高分辨视频的播放占比提升,流量也会随之增加。

货车-两轮车碰撞事故典型场景提取及关联规则分析

基于视频信息提取了货车-两轮车碰撞典型事故场景,对事故场景中不同事故因素间的相关性进行了挖掘以补充代表事故遗漏的事故特征。应用210例带视频信息的货车-两轮车碰撞事故案例获取相关事故信息,采用K-modes聚类方法对事故案例进行聚类得到典型事故场景,采用关联规则方法对每个事故场景中事故因素之间的关联度进行分析。结果表明:货车-两轮车事故可分为4种典型场景,即2种十字路口事故,1种直行道路事故以及1种丁字路口事故。十字路口事故场景中事故特征与存在视觉障碍关联度较高,且在十字路口事故中“货车左转”所带来的风险也比较高;直行道路事故中货车制动规避与道路上是否有信号灯存在关联性;丁字路口事故中型货车转向规避与骑车人遭到碾压之间存在关联性。该研究结果可以为两轮车骑车人安全对策和安全测试场景提供参考。

基于视频图像驱动的驾驶人注意力估计方法

驾驶人视觉注意力的深入研究对于预测不安全驾驶行为和理解驾驶行为具有重要意义。为此,提出一种基于视频图像驱动的驾驶人注意力估计方法,以估计驾驶人在行车时注意到视域内的行人或车辆等各种对象。该方法利用深度神经网络学习交通场景视频与驾驶员注意力特征之间的映射关系,并融入引导学习模块来提取与驾驶员注意力最相关的特征。考虑到驾驶的动态性,使用动态交通场景视频作为模型输入,设计时空特征提取模块。在稀疏、密集、低照度等常见的交通场景中,将估计的驾驶员注意力模型与收集的驾驶员注意力数据点进行对比。实验结果表明,所提方法能够准确估计驾驶员在驾驶过程中的注意力,对于预测不安全驾驶行为以及促进人们更好地理解驾驶行为具有重要的理论和实用价值。

面向实时视频流传输的边缘计算资源分配方案

随着直播行业的兴起,基站视频流量激增问题日趋严重。为此,设计一种面向视频传输的移动边缘计算任务卸载联合资源分配方案,将原问题可分解为实时视频任务卸载决策问题和移动边缘计算资源分配问题。该方案利用可伸缩视频编码技术的分层特性,通过编码将视频流转换为基础层和增强层,以适应用户信道间的差异性,降低视频上传运输成本,提高用户体验质量。考虑到移动边缘计算就近服务用户的特点,该方案将视频任务卸载至边缘端进行解析,用于解决视频层数选择、卸载基站选择以及联合资源优化问题。仿真实验表明,与传统方案相比,所提方案的卸载可行性明显更高,且有效减少了传输时延和能源消耗,提高了移动用户的视频传输性能。

一种基于HTTP/3传输特性的加密视频识别方法

视频流量逐渐在网络中占据主导地位,且视频平台大多对其进行加密传输。虽然加密传输视频可以有效保护用户隐私,但是也增加了监管有害视频传播的难度.现有的加密视频识别方法基于TCP(Transmission Control Protocol)传输协议头部信息和HTTP/1.1(Hypertext Transfer Protocol Version1.1)的传输模式,提取应用层音视频数据单元传输长度序列来实现视频识别.但是随着基于UDP(User Datagram Protocol)的QUIC(Quick UDP Internet Connections)协议及基于QUIC实现的HTTP/3(Hypertext Transfer Protocol Version 3)协议应用于视频传输,已有方法不再适用.HTTP/3协议缺少类似TCP的头部信息,且使用了多路复用机制,并对几乎所有数据进行了加密,此外,视频平台开始使用多片段合并分发技术,这给从网络流量中精准识别加密视频带来了巨大挑战。本文基于HTTP/3协议中的控制信息特征,提出了从HTTP/3加密视频流中提取数据传输特征并进行修正的方法,最大程度复原出应用层音视频长度特征.面向多片段合并分发导致的海量匹配问题,本文基于明文指纹库设计了键值数据库来实现视频的快速识别.实验结果表明,本文提出的基于HTTP/3传输特性的加密视频识别方法能够在包含36万个真实视频指纹的YouTube大规模指纹库中达到接近99%的准确率,100%的精确率以及99.32%的F1得分,对传输过程中加人了填充顿的Facebook平台,在包含28万个真实视频指纹的大规模指纹库中达到95%的准确率、100%的精确率以及96.45%的F1得分,在具有同样特性的Instagram平台中,最高可达到97.57%的F1得分,且本方法在所有指纹库中的平均视频识别时间均低于0.4秒.本文的方法首次解决了使用HTTP/3传输的加密视频在大规模指纹库场景中的识别问题,具有很强的实用性和通用性.

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。

基于YOLOv5的车流量检测研究

对基于卷积神经网络的目标检测以及流量检测原理进行研究,选择在实时性、精确性等方面具有良好表现的YOLOv5作为目标检测模型。然后采用Labelme对采集到的交通视频数据进行标注,以PyCharm作为集成开发环境进行编程,实现了车辆目标检测与流量检测两个主要功能。测试运行结果表明,不管在白天还是夜晚,在各种干扰条件下,基于YOLOv5的视频车流量检测软件都可以准确检测出当前车流量,具有较强的鲁棒性、实时性和精确性,可满足实际应用的需求。

基于烟雾运动特征的高速公路火灾检测研究

目前高速公路火灾检测方法仍存在检测精度低、计算资源消耗多的问题。利用烟雾的运动特征结合深度网络,设计一个高速公路火灾检测方法。利用高斯混合背景建模提取视频中的运动前景,对运动前景进行感兴趣区域筛选,得到疑似烟雾区域,再对该区域进行HSV颜色空间分析,确定该区域是否存在烟雾。对于存在烟雾的视频帧,通过构建的具有5万余个目标标注框的高速公路专用火灾烟雾数据集,利用YOLOv5目标检测方法,达到了90.16%的平均检测精度(mAP)。所提方法避免了逐帧对视频流的火灾检测,极大地减少了计算资源的浪费,具有研究与实际工程应用价值。

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。