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 con...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.展开更多
Real-time video transport over wireless Internet faces many challenges due to the heterogeneous environment including wireline and wireless networks. A robust network condition classification algorithm using multiple ...Real-time video transport over wireless Internet faces many challenges due to the heterogeneous environment including wireline and wireless networks. A robust network condition classification algorithm using multiple end-to-end metrics and Support Vector Machine (SVM) is proposed to classify different network events and model the transition pattern of network conditions. End-to-end Quality-of-Service (QoS) mechanisms like congestion control, error control, and power control can benefit from the network condition information and react to different network situations appropriately. The proposed network condition classifica- tion algorithm uses SVM as a classifier to cluster different end-to-end metrics such as end-to-end delay, delay jitter, throughput and packet loss-rate for the UDP traffic with TCP-friendly Rate Control (TFRC), which is used for video transport. The algorithm is also flexible for classifying different numbers of states representing different levels of network events such as wireline congestion and wireless channel loss. Simulation results using network simulator 2 (ns2) showed the effectiveness of the proposed scheme.展开更多
The scalable extension of H.264/AVC, known as scalable video coding or SVC, is currently the main focus of the Joint Video Team’s work. In its present working draft, the higher level syntax of SVC follows the design ...The scalable extension of H.264/AVC, known as scalable video coding or SVC, is currently the main focus of the Joint Video Team’s work. In its present working draft, the higher level syntax of SVC follows the design principles of H.264/AVC. Self-contained network abstraction layer units (NAL units) form natural entities for packetization. The SVC specification is by no means finalized yet, but nevertheless the work towards an optimized RTP payload format has already started. RFC 3984, the RTP payload specification for H.264/AVC has been taken as a starting point, but it became quickly clear that the scalable features of SVC require adaptation in at least the areas of capability/operation point signaling and documentation of the extended NAL unit header. This paper first gives an overview of the history of scalable video coding, and then reviews the video coding layer (VCL) and NAL of the latest SVC draft specification. Finally, it discusses different aspects of the draft SVC RTP payload format, in- cluding the design criteria, use cases, signaling and payload structure.展开更多
This paper presents a streaming system using scalable video coding based on H.264/AVC. The system provides a congestion control algorithm supported by channel bandwidth estimation of the client. It uses retransmission...This paper presents a streaming system using scalable video coding based on H.264/AVC. The system provides a congestion control algorithm supported by channel bandwidth estimation of the client. It uses retransmission only for packets of the base layer to disburden the congested network. The bandwidth estimation allows for adjusting the transmission rate quickly to the current available bandwidth of the network. Compared to binomial congestion control, the proposed system allows for shorter start-up times and data rate adaptation. The paper describes the components of this streaming system and the results of experiments showing that the proposed approach works effectively for streaming video.展开更多
This paper chiefly introduces the model of Video Conferencing System, H.323 standard of ITU- T and RTP/RTCP protocol, and realizes a WAN- Oriented Video Conferencing System—VCSW (Video Conferencing System on WAN). In...This paper chiefly introduces the model of Video Conferencing System, H.323 standard of ITU- T and RTP/RTCP protocol, and realizes a WAN- Oriented Video Conferencing System—VCSW (Video Conferencing System on WAN). In VCSW, RTP/RTCP pro- tocol is used for transporting media data and video conference on WAN can be established, so VCSW has abroad application scope. This paper stresses analyzing RTP/RTCP protocol, realizes it and modifies some details of it according to our device condition: re- duces some fields of RTP and RTCP header, removes some information managed by RTP/RTCP. Consequently in testing of lab, good effect is achieved.展开更多
目的探讨微信实时远程视频评估体系在危重新生儿转运中的应用效果。方法选择2021年3月—2022年10月广西壮族自治区妇幼保健院急诊科转诊的新生儿450例为对象,随机数字表法分为对照组和观察组。对照组150例采用电话沟通方式评估,观察组30...目的探讨微信实时远程视频评估体系在危重新生儿转运中的应用效果。方法选择2021年3月—2022年10月广西壮族自治区妇幼保健院急诊科转诊的新生儿450例为对象,随机数字表法分为对照组和观察组。对照组150例采用电话沟通方式评估,观察组300例采用微信实时远程视频评估体系。观察组交替选择新生儿危重病例评分(neonatal critical case score,NICS)组和新生儿转运生理稳定指数(neonatal transport physiological stability ndex,TRIPS)评估进行危重评分,分为NICS组和TRIPS组两个亚组,各150例。比较两组稳定时间、机械通气时间及平均住院时间、转运不良事件发生率、死亡率及入院7 d内的死亡率。结果两组平均住院时间比较,差异无统计意义(P>0.05);观察组危重新生儿转运中生命体征稳定时间、机械通气时间短于对照组(P<0.05)。观察组根据评分系统不同进行分析,NICS组及TRIPS组生命体征稳定时间、平均住院时间比较,差异无统计意义(P>0.05);NICS组危重新生儿转运中评分所需时间长于TRIPS组(P<0.05);NICS组机械通气时间低于TRIPS组(P<0.05)。两组总转运不良事件发生率比较,差异无统计学意义(P>0.05);观察组入院7 d内死亡率低于对照组(P<0.05)。结论微信实时远程视频评估体系用于危重新生儿转运中可获得良好的效果,且TRIPS评估体系更加便捷、简洁。展开更多
为了实时识别快速路交织区拥堵瓶颈的形成及其诱发因素,基于无人机航拍视频构建车辆轨迹数据,提出一种融合交通流不稳定性分析的交织区拥堵识别方法。识别方法由车辆轨迹提取、扰动感知模型和拥堵风险指数构建3个阶段构成。首先,通过YOL...为了实时识别快速路交织区拥堵瓶颈的形成及其诱发因素,基于无人机航拍视频构建车辆轨迹数据,提出一种融合交通流不稳定性分析的交织区拥堵识别方法。识别方法由车辆轨迹提取、扰动感知模型和拥堵风险指数构建3个阶段构成。首先,通过YOLOv4(You Only Look Once,Version 4)网络训练航拍小目标权重检测俯拍车辆,关联外观与运动特征以跟踪车辆轨迹,从而提取无人机航拍视频中的精细车辆轨迹。然后,通过提取车辆微观速度、变道、冲突信息建立车速扰动和变道交织扰动感知模型。最后,采用熵值法结合扰动信息与平均车速构建归一化的拥堵风险指数,根据交织流的拥堵风险指数识别拥堵。本文采集广州大桥数据进行案例分析与测试验证。研究结果表明:学习了小目标特征的网络在航拍场景测试的误检率和少检率均低于5%,所提取的车辆轨迹连续稳定;在交织区拥堵识别评价中,本文方法的F1值达到97.85%,明显优于基本参数识别方法,在各路段中具有较高的识别准确度和算法鲁棒性;相比平均速度指标,所提出的拥堵风险指数能够更精细灵敏地反映短时和局部的拥堵,并能够从平均车速、个体车速差异和变道交织3个维度中识别多种因素引起的交织区交通瓶颈。研究结果可为城市重点路段交通诱导与优化提供技术基础。展开更多
文摘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.
基金Project supported by the Croucher Foundation Fellowship fromHong Kong, China
文摘Real-time video transport over wireless Internet faces many challenges due to the heterogeneous environment including wireline and wireless networks. A robust network condition classification algorithm using multiple end-to-end metrics and Support Vector Machine (SVM) is proposed to classify different network events and model the transition pattern of network conditions. End-to-end Quality-of-Service (QoS) mechanisms like congestion control, error control, and power control can benefit from the network condition information and react to different network situations appropriately. The proposed network condition classifica- tion algorithm uses SVM as a classifier to cluster different end-to-end metrics such as end-to-end delay, delay jitter, throughput and packet loss-rate for the UDP traffic with TCP-friendly Rate Control (TFRC), which is used for video transport. The algorithm is also flexible for classifying different numbers of states representing different levels of network events such as wireline congestion and wireless channel loss. Simulation results using network simulator 2 (ns2) showed the effectiveness of the proposed scheme.
文摘The scalable extension of H.264/AVC, known as scalable video coding or SVC, is currently the main focus of the Joint Video Team’s work. In its present working draft, the higher level syntax of SVC follows the design principles of H.264/AVC. Self-contained network abstraction layer units (NAL units) form natural entities for packetization. The SVC specification is by no means finalized yet, but nevertheless the work towards an optimized RTP payload format has already started. RFC 3984, the RTP payload specification for H.264/AVC has been taken as a starting point, but it became quickly clear that the scalable features of SVC require adaptation in at least the areas of capability/operation point signaling and documentation of the extended NAL unit header. This paper first gives an overview of the history of scalable video coding, and then reviews the video coding layer (VCL) and NAL of the latest SVC draft specification. Finally, it discusses different aspects of the draft SVC RTP payload format, in- cluding the design criteria, use cases, signaling and payload structure.
文摘This paper presents a streaming system using scalable video coding based on H.264/AVC. The system provides a congestion control algorithm supported by channel bandwidth estimation of the client. It uses retransmission only for packets of the base layer to disburden the congested network. The bandwidth estimation allows for adjusting the transmission rate quickly to the current available bandwidth of the network. Compared to binomial congestion control, the proposed system allows for shorter start-up times and data rate adaptation. The paper describes the components of this streaming system and the results of experiments showing that the proposed approach works effectively for streaming video.
基金Approval No. of the National Natural ScienceFund: 60573182
文摘This paper chiefly introduces the model of Video Conferencing System, H.323 standard of ITU- T and RTP/RTCP protocol, and realizes a WAN- Oriented Video Conferencing System—VCSW (Video Conferencing System on WAN). In VCSW, RTP/RTCP pro- tocol is used for transporting media data and video conference on WAN can be established, so VCSW has abroad application scope. This paper stresses analyzing RTP/RTCP protocol, realizes it and modifies some details of it according to our device condition: re- duces some fields of RTP and RTCP header, removes some information managed by RTP/RTCP. Consequently in testing of lab, good effect is achieved.
文摘目的探讨微信实时远程视频评估体系在危重新生儿转运中的应用效果。方法选择2021年3月—2022年10月广西壮族自治区妇幼保健院急诊科转诊的新生儿450例为对象,随机数字表法分为对照组和观察组。对照组150例采用电话沟通方式评估,观察组300例采用微信实时远程视频评估体系。观察组交替选择新生儿危重病例评分(neonatal critical case score,NICS)组和新生儿转运生理稳定指数(neonatal transport physiological stability ndex,TRIPS)评估进行危重评分,分为NICS组和TRIPS组两个亚组,各150例。比较两组稳定时间、机械通气时间及平均住院时间、转运不良事件发生率、死亡率及入院7 d内的死亡率。结果两组平均住院时间比较,差异无统计意义(P>0.05);观察组危重新生儿转运中生命体征稳定时间、机械通气时间短于对照组(P<0.05)。观察组根据评分系统不同进行分析,NICS组及TRIPS组生命体征稳定时间、平均住院时间比较,差异无统计意义(P>0.05);NICS组危重新生儿转运中评分所需时间长于TRIPS组(P<0.05);NICS组机械通气时间低于TRIPS组(P<0.05)。两组总转运不良事件发生率比较,差异无统计学意义(P>0.05);观察组入院7 d内死亡率低于对照组(P<0.05)。结论微信实时远程视频评估体系用于危重新生儿转运中可获得良好的效果,且TRIPS评估体系更加便捷、简洁。
文摘为了实时识别快速路交织区拥堵瓶颈的形成及其诱发因素,基于无人机航拍视频构建车辆轨迹数据,提出一种融合交通流不稳定性分析的交织区拥堵识别方法。识别方法由车辆轨迹提取、扰动感知模型和拥堵风险指数构建3个阶段构成。首先,通过YOLOv4(You Only Look Once,Version 4)网络训练航拍小目标权重检测俯拍车辆,关联外观与运动特征以跟踪车辆轨迹,从而提取无人机航拍视频中的精细车辆轨迹。然后,通过提取车辆微观速度、变道、冲突信息建立车速扰动和变道交织扰动感知模型。最后,采用熵值法结合扰动信息与平均车速构建归一化的拥堵风险指数,根据交织流的拥堵风险指数识别拥堵。本文采集广州大桥数据进行案例分析与测试验证。研究结果表明:学习了小目标特征的网络在航拍场景测试的误检率和少检率均低于5%,所提取的车辆轨迹连续稳定;在交织区拥堵识别评价中,本文方法的F1值达到97.85%,明显优于基本参数识别方法,在各路段中具有较高的识别准确度和算法鲁棒性;相比平均速度指标,所提出的拥堵风险指数能够更精细灵敏地反映短时和局部的拥堵,并能够从平均车速、个体车速差异和变道交织3个维度中识别多种因素引起的交织区交通瓶颈。研究结果可为城市重点路段交通诱导与优化提供技术基础。
