Using microscopic video data measures for driver behavior analysis during adverse winter weather:opportunities and challenges

This paper presents a driver behavior analysis using microscopic video data measures including vehicle speed, lane-changing ratio, and time to collision. An analytical framework was developed to evaluate the effect of adverse winter weather conditions on highway driving behavior based on automated （computer） and manual methods. The research was conducted through two case studies. The first case study was conducted to evaluate the feasibility of applying an au- tomated approach to extracting driver behavior data based on 15 video recordings obtained in the winter 2013 at three dif- ferent locations on the Don Valley Parkway in Toronto, Canada. A comparison was made between the automated approach and manual approach, and issues in collecting data using the automated approach under winter conditions were identified. The second case study was based on high quality data collected in the winter 2014, at a location on Highway 25 in Montreal, Canada. The results demonstrate the effectiveness of the automated analytical framework in analyzing driver behavior, as well as evaluating the impact of adverse winter weather conditions on driver behavior. This approach could be applied to evaluate winter maintenance strategies and crash risk on highways during adverse winter weather conditions.

A Novel Video Data-Source Authentication Model Based on Digital Watermarking and MAC in Multicast

A novel video data authentication model based on digital video watermarking and MAC （message authentication code） in multicast protocol is proposed in this paper, The digital watermarking which composes of the MAC of the significant vid eo content, the key and instant authentication data is embedded into the insignificant video component by the MLUT （modified look-up table） video watermarking technology. We explain a method that does not require storage of each data packet for a time, thus making receiver not vulnerable to DOS （denial of service） attack. So the video packets can be authenticated instantly without large volume buffer in the receivers. TESLA （timed efficient stream loss tolerant authentication） does not explain how to select the suitable value for d, which is an important parameter in multicast source authentication. So we give a method to calculate the key disclosure delay （number of intervals）. Simulation results show that the proposed algorithms improve the performance of data source authentication in multicast.

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.

Optimization-Based Fragmental Transmission Method for Video Data in Opportunistic Networks

Multimedia data have become popularly transmitted content in opportunistic networks. A large amount of video data easily leads to a low delivery ratio. Breaking up these big data into small pieces or fragments is a reasonable option. The size of the fragments is critical to transmission efficiency and should be adaptable to the communication capability of a network. We propose a novel communication capacity calculation model of opportunistic network based on the classical random direction mobile model, define the restrain facts model of overhead, and present an optimal fragment size algorithm. We also design and evaluate the methods and algorithms with video data fragments disseminated in a simulated environment. Experiment results verified the effectiveness of the network capability and the optimal fragment methods.

StreamTune： dynamic resource scheduling approach for workload skew in video data center

Video surveillance applications need video data center to provide elastic virtual machine （VM） provisioning. However, the workloads of the VMs are hardly to be predicted for online video surveillance service. The unknown arrival workloads easily lead to workload skew among VMs. In this paper, we study how to balance the workload skew on online video surveillance system. First, we design the system framework for online surveillance service which con- sists of video capturing and analysis tasks. Second, we propose StreamTune, an online resource scheduling approach for workload balancing, to deal with irregular video analysis workload with the minimum number of VMs. We aim at timely balancing the workload skew on video analyzers without depending on any workload prediction method. Furthermore, we evaluate the performance of the proposed approach using a traffic surveillance application. The experimental results show that our approach is well adaptive to the variation of workload and achieves workload balance with less VMs.

Minimizing Resource Cost for Camera Stream Scheduling in Video Data Center

Video surveillance service, which receives live streams from IP cameras and forwards the streams to end users, has become one of the most popular services of video data center. The video data center focuses on minimizing the resource cost during resource provisioning for the service. However, little of the previous work comprehensively considers the bandwidth cost optimization of both upload and forwarding streams, and the capacity of the media server. In this paper, we propose an efficient resource scheduling approach for online multi-camera video forwarding, which tries to optimize the resource sharing of media servers and the networks together. Firstly, we not only provide a fine-grained resource usage model for media servers, but also evaluate the bandwidth cost of both upload and forwarding streams. Without loss of generality, we utilize two resource pricing models with different resource cost functions to evaluate the resource cost： the linear cost function and the non-linear cost functions. Then, we formulate the cost minimization problem as a constrained integer programming problem. For the linear resource cost function, the drift-plus-penalty optimization method is exploited in our approach. For non-linear resource cost functions, the approach employs a heuristic method to reduce both media server cost and bandwidth cost. The experimental results demonstrate that our approach obviously reduces the total resource costs on both media servers and networks simultaneously.

Design and Implementation of GIS Data Production System for Expressway Video and Road Infostructure

The expressway is necessary for the development of the modern transportation industry, and the level of expressway construction reflects the overall grade of national or regional economic development. In order to process the expressway road property data information, based on the current mainstream Windows operating system, this study utilizes Geographic Information System (GIS) development technology, road video processing technology, and spatial data mining method to design and develop an expressway video and road infostructure GIS data production system. The system designs a multi-layer distributed application model in accordance with the ideas and methods of GIS engineering and the characteristics of road production data. In addition, according to the characteristics and specification requirements of basic geographic data, the road production database of spatial data and attribute data integrated storage is constructed by combining database and spatial data engine. Through the development of the GIS data production system for expressway video and road infostructure, various functions such as generation of road property data, dynamic management of road infostructure, and visualization of spatial information have been realized. The system focuses on improving the production efficiency and automation level of expressway production data and meets the construction requirements for modernization, informatization, and intelligence of expressways.

Recommendations for Big Data in Online Video Quality of Experience Assessment

Real-time video application usage is increasing rapidly. Hence, accurate and efficient assessment of video Quality of Experience (QoE) is a crucial concern for end-users and communication service providers. After considering the relevant literature on QoS, QoE and characteristics of video trans-missions, this paper investigates the role of big data in video QoE assessment. The impact of QoS parameters on video QoE are established based on test-bed experiments. Essentially big data is employed as a method to establish a sensible mapping between network QoS parameters and the resulting video QoE. Ultimately, based on the outcome of experiments, recommendations/re- quirements are made for a Big Data-driven QoE model.

基于VideoCharger视频点播系统的设计与实现

探讨了VideoCharger的逻辑组成、交互与配置方式 ,及详细的资源需求分析 ,给出了据此建立VOD系统的系列解决方案。

Human Stress Recognition by Correlating Vision and EEG Data

Because stress has such a powerful impact on human health,we must be able to identify it automatically in our everyday lives.The human activity recognition(HAR)system use data from several kinds of sensors to try to recognize and evaluate human actions automatically recognize and evaluate human actions.Using the multimodal dataset DEAP(Database for Emotion Analysis using Physiological Signals),this paper presents deep learning(DL)technique for effectively detecting human stress.The combination of vision-based and sensor-based approaches for recognizing human stress will help us achieve the increased efficiency of current stress recognition systems and predict probable actions in advance of when fatal.Based on visual and EEG(Electroencephalogram)data,this research aims to enhance the performance and extract the dominating characteristics of stress detection.For the stress identification test,we utilized the DEAP dataset,which included video and EEG data.We also demonstrate that combining video and EEG characteristics may increase overall performance,with the suggested stochastic features providing the most accurate results.In the first step,CNN(Convolutional Neural Network)extracts feature vectors from video frames and EEG data.Feature Level(FL)fusion that combines the features extracted from video and EEG data.We use XGBoost as our classifier model to predict stress,and we put it into action.The stress recognition accuracy of the proposed method is compared to existing methods of Decision Tree(DT),Random Forest(RF),AdaBoost,Linear Discriminant Analysis(LDA),and KNearest Neighborhood(KNN).When we compared our technique to existing state-of-the-art approaches,we found that the suggested DL methodology combining multimodal and heterogeneous inputs may improve stress identification.

QoS Aware Multicast Routing Protocol for Video Transmission in Smart Cities

In recent years,Software Defined Networking(SDN)has become an important candidate for communication infrastructure in smart cities.It produces a drastic increase in the need for delivery of video services that are of high resolution,multiview,and large-scale in nature.However,this entity gets easily influenced by heterogeneous behaviour of the user’s wireless link features that might reduce the quality of video stream for few or all clients.The development of SDN allows the emergence of new possibilities for complicated controlling of video conferences.Besides,multicast routing protocol with multiple constraints in terms of Quality of Service(QoS)is a Nondeterministic Polynomial time(NP)hard problem which can be solved only with the help of metaheuristic optimization algorithms.With this motivation,the current research paper presents a new Improved BlackWidow Optimization with Levy Distribution model(IBWO-LD)-based multicast routing protocol for smart cities.The presented IBWO-LD model aims at minimizing the energy consumption and bandwidth utilization while at the same time accomplish improved quality of video streams that the clients receive.Besides,a priority-based scheduling and classifier model is designed to allocate multicast request based on the type of applications and deadline constraints.A detailed experimental analysis was carried out to ensure the outcomes improved under different aspects.The results from comprehensive comparative analysis highlighted the superiority of the proposed IBWO-LD model over other compared methods.

VB环境下Audio/Video压缩数据流播放技术的应用

介绍了在 Windwos操作系统中 ,利用 VB自身条件和原有控件 ,灵活调用 Windows下的动态链接库开发

基于Video-GIS数据采集系统的小案管控模式研究

小案管控模式是指对一些轻微的刑事案件以及部分影响社会稳定的治安案件进行管理和控制的方式。随着时代的发展,犯罪态势的变化,大案要案得到了有效遏制,而小案侦破率低,警务关注有限,防控乏力,一直是基层公安机关面临的难题。公安机关应当分析小案的发案机理,借助新型警务技术Video-GIS数据采集系统,在对高发的小案进行数据建模的基础上,对重点地区、重点人员进行管理和控制,变案后打击为案前预测、同步监控,从而提升警务效能。

Stereo Video Transmission Using LDPC Code

Stereo video is widely used because it can provide depth information. However, it is difficult to store and transmit stereo video due to the huge data amount. So, high efficient channel encoding algorithm and proper transmission strategy is needed to deal with the video transmission over limited bandwidth channel. In this paper, unequal error protection (UEP) based on low density parity check (LDPC) code was used to transmit stereo video over wireless channel with limited bandwidth. Different correction level LDPC code was used according to the importance of video stream to reconstruction at the receiver. Simulation result shows that the proposed transmission scheme increases the PSNR of reconstructed image, and improves the subjective effect.

VIDEO MULTI-TARGET TRACKING BASED ON PROBABILISTIC GRAPHICAL MODEL

In the technique of video multi-target tracking,the common particle filter can not deal well with uncertain relations among multiple targets.To solve this problem,many researchers use data association method to reduce the multi-target uncertainty.However,the traditional data association method is difficult to track accurately when the target is occluded.To remove the occlusion in the video,combined with the theory of data association,this paper adopts the probabilistic graphical model for multi-target modeling and analysis of the targets relationship in the particle filter framework.Ex-perimental results show that the proposed algorithm can solve the occlusion problem better compared with the traditional algorithm.

短视频消费时长能够反映就业率吗?

鉴于现行就业统计方式不能及时、准确反映经济运行形势,如何利用大数据对现有统计指标进行补充成为一个重要问题。鉴于短视频消费已经成为当今社会主要的休闲娱乐方式,文章将短视频消费纳入效用函数,通过失业人员与雇员在时间分配上的差异,揭示全社会用于观看短视频的时间与就业率之间存在的理论联系。通过计量分析,研究发现基于抖音在线人数计算的估计值与描述失业者寻找工作行为的百度搜索指数呈现正相关关系,可以借助短视频观看时长变动说明失业者寻找工作行为的变化。基于抖音在线人数估计的劳动参与率与16—24岁城镇调查失业率存在显著负相关关系,两者的皮尔逊相关系数为-0.862,因此估计的劳动参与率可以作为城镇调查失业率,特别是16—24岁劳动力调查失业率的补充。

航空辐射数据热力图与视频融合方法

随着核能技术的广泛应用,核安全与应急监测的重要性日益凸显,核应急航空监测成为国家核应急体系的重要组成部分。在核应急航空监测过程中,获取并分析航空辐射数据与视频数据对于快速监测辐射状况、圈定辐射污染区域具有重要意义。其中,航空监测视频数据可以全面直观地获取目标区域地面影像,并大范围展现目标区域地面场景实态,结合放射性异常时刻的视频帧可快速精确地分析异常的成因,具有高时效性的特点。尽管视频融合技术在城市安全、交通管控等领域得到广泛应用,但在核应急航空监测领域中,视频融合技术研究应用较少,存在航空辐射数据与视频之间关联性较差的问题。传统方法中,应急人员需要手动搜寻视频关键帧,分析地表地貌特征,效率较低。设计并实现将航空辐射数据以热力图的形式与视频进行融合的程序,总体上遵循“视频-图像-视频”变换过程。通过有效提取视频关键帧图像及关注数据信息,建立数值与色度映射关系,分析飞行方向,绘制热力图,并将热力图与视频帧融合显示,实现航空辐射数据热力图与视频的融合。这种融合方法的应用可以帮助技术人员更快速地识别和分析辐射热点及污染区域,为制定应急措施提供有力支持。因此,该研究对于提高航空辐射数据与视频数据的关联度,实现快速分析核事故影响具有重要意义。此外,所采用的方法也可应用到铀矿勘查、辐射环境航空调查等工作中。

基于电力载波与云计算的视频监控系统设计

针对目前视频监控系统不断升级改造存在的布线工作量大、布线成本高、无法存储及计算海量视频文件的问题,该文提出基于电力载波技术和云计算框架的视频监控系统。视频数据经过调制解调、振荡、放大、耦合等过程,利用电力线网络传输到各分控点,通过互联网传送到云端,利用云端强大的存储、计算能对视频数据进行各种处理。设计中硬件部分的ARM11微处理器主要负责视频数据的采集、编码并封包传送到PLC调制解调模块,完成数据收发控制和系统任务调度。软件部分主要包括开发系统搭建、USB摄像头驱动、视频数据采集、LCD实时显示、网络传输和双核数据处理等。该文的设计充分结合电力载波和云计算的特点,在视频监控领域中具有较高的应用价值。

中国石油井下作业数字化监督发展问题与对策

阐述了中国石油井下作业数字化监督在监督人员数字化管理、施工队伍数字化监督、施工作业数字化监督、井场环境数字化监督4个方面的发展现状。分析了井下作业数字化监督发展面临数字化监督管理制度不健全、监督数据标准不统一、数字化智能监督技术不完善、缺少统一的数字化智能监督平台、监督数据价值挖掘不深入5个方面的主要问题。提出了制定井下作业数字化监督管理制度、建立井下作业监督数据标准、攻关数字化智能监督关键技术、研发井下作业数字化智能监督平台、提升监督大数据分析应用水平5项数字化监督发展对策。展望了井下作业数字化监督建设发展后在提升监督人员履职效率、降低监督人力资源成本、提升工程质量、保障施工安全与环保4个方面的预期成效。

基于视频AI的互联网数据中心机房智慧管理技术研究

人工智能正在重塑电信市场的竞争环境和盈利模式,电信运营商急需战略转型,通过优化价值链争取竞争优势以赶上数字经济发展的战略机遇。本文使用文献研究法和案例研究法研究了中国电信的互联网数据中心使用视频人工智能进行智慧化管理的情况,发现虚拟价值链逐步超越传统价值链,得出对人工智能技术的恰当运用有助于电信企业长远发展的结论,可供电信运营商在智能数字时代发展相关业务时参考。