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.

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.

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.

Analysis of video head impulse test saccades data in patients with vestibular migraine or probable vestibular migraine

Objective:Saccades accompanied by normal gain in video head impulse tests(vHIT)are often observed in patients with vestibular migraine(VM).However,they are not considered as an independent indicator,reducing their utility in diagnosing VM.To better understand clinical features of VM,it is necessary to understand raw saccades data.Methods:Fourteen patients with confirmed VM,45 patients with probable VM(p-VM)and 14 agematched healthy volunteers were included in this study.Clinical findings related to spontaneous nystagmus(SN),positional nystagmus(PN),head-shaking nystagmus(HSN),caloric test and vHIT were recorded.Raw saccades data were exported and numbered by their sequences,and their features analyzed.Results:VM patients showed no SN,PN or HSN,and less than half of them showed unilateral weakness(UW)on caloric test.The first saccades from lateral semicircular canal stimulation were the most predominant for both left and right sides.Neither velocity nor time parameters were significantly different when compared between the two sides.Most VM patients(86%)exhibited small saccades,around 35%of the head peak velocity,with a latency of 200e400 ms.Characteristics of saccades were similar in patients with p-VM.Only four normal subjects showed saccades,all unilateral and seemingly random.Conclusions:Small saccades involving bilateral semicircular canals with a scattered distribution pattern are common in patients with VM and p-VM.

Research on Big Data and Artificial Intelligence Aided Decision-Making Mechanism with the Applications on Video Website Homemade Program Innovation

In this paper, we conduct research on the big data and the artificial intelligence aided decision-making mechanism with the applications on video website homemade program innovation. Make homemade video shows new media platform site content production with new possible, as also make the traditional media found in Internet age, the breakthrough point of the times. Site homemade video program, which is beneficial to reduce copyright purchase demand, reduce the cost, avoid the homogeneity competition, rich advertising marketing at the same time, improve the profit pattern, the organic combination of content production and operation, complete the strategic transformation. On the basis of these advantages, once the site of homemade video program to form a brand and a higher brand influence. Our later research provides the literature survey for the related issues.

Improved Video Steganography with Dual Cover Medium,DNA and Complex Frames

The most valuable resource on the planet is no longer oil,but data.The transmission of this data securely over the internet is another challenge that comes with its ever-increasing value.In order to transmit sensitive information securely,researchers are combining robust cryptography and steganographic approaches.The objective of this research is to introduce a more secure method of video steganography by using Deoxyribonucleic acid(DNA)for embedding encrypted data and an intelligent frame selection algorithm to improve video imperceptibility.In the previous approach,DNA was used only for frame selection.If this DNA is compromised,then our frames with the hidden and unencrypted data will be exposed.Moreover the frame selected in this way were random frames,and no consideration was made to the contents of frames.Hiding data in this way introduces visible artifacts in video.In the proposed approach rather than using DNA for frame selection we have created a fakeDNA out of our data and then embedded it in a video file on intelligently selected frames called the complex frames.Using chaotic maps and linear congruential generators,a unique pixel set is selected each time only from the identified complex frames,and encrypted data is embedded in these random locations.Experimental results demonstrate that the proposed technique shows minimum degradation of the stenographic video hence reducing the very first chances of visual surveillance.Further,the selection of complex frames for embedding and creation of a fake DNA as proposed in this research have higher peak signal-to-noise ratio(PSNR)and reduced mean squared error(MSE)values that indicate improved results.The proposed methodology has been implemented in Matlab.

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.

Reversible Data Hiding Based on Run-Level Coding in H.264/AVC Video Streams

This paper presents a reversible data hiding(RDH)method,which is designed by combining histogram modification(HM)with run-level coding in H.264/advanced video coding(AVC).In this scheme,the run-level is changed for embedding data into H.264/AVC video sequences.In order to guarantee the reversibility of the proposed scheme,the last nonzero quantized discrete cosine transform(DCT)coefficients in embeddable 4×4 blocks are shifted by the technology of histogram modification.The proposed scheme is realized after quantization and before entropy coding of H.264/AVC compression standard.Therefore,the embedded information can be correctly extracted at the decoding side.Peak-signal-noise-to-ratio(PSNR)and Structure similarity index(SSIM),embedding payload and bit-rate variation are exploited to measure the performance of the proposed scheme.Experimental results have shown that the proposed scheme leads to less SSIM variation and bit-rate increase.

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.

A Multi-objective Optimization Data Scheduling Algorithm for P2P Video Streaming

In P2P video streaming, each peer requests its wanted streaming data from others and responses others' requests by its data scheduling algorithm. Recent years, some data scheduling algorithms are proposed either to optimize the perceived video quality, or to optimize the network throughput. However, optimizing the perceived video quality may lead to low utilization of the senders'upload capacity. On the other hand, optimizing the network throughput may lead to the degrading perceived quality, for some emergent data may not be transmitted in time. In this paper, to improve the two objectives simultaneously, we formulate the data scheduling problem as a multi-objective model. In the formulation, we not only consider the segment quality and emergency which affect the perceived video quality, but also consider the rarity of the segments, which influences the network throughput. Then, we propose a distributed data scheduling algorithm to solve the multi-objective problem in polynomial time. Through simulations, we show the proposed algorithm outperforms other conventional algorithms in perceived video quality and utilization of peers' upload capacity.

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.

Deeply‐Recursive Attention Network for video steganography

Video steganography plays an important role in secret communication that conceals a secret video in a cover video by perturbing the value of pixels in the cover frames.Imperceptibility is the first and foremost requirement of any steganographic approach.Inspired by the fact that human eyes perceive pixel perturbation differently in different video areas,a novel effective and efficient Deeply‐Recursive Attention Network(DRANet)for video steganography to find suitable areas for information hiding via modelling spatio‐temporal attention is proposed.The DRANet mainly contains two important components,a Non‐Local Self‐Attention(NLSA)block and a Non‐Local Co‐Attention(NLCA)block.Specifically,the NLSA block can select the cover frame areas which are suitable for hiding by computing the correlations among inter‐and intra‐cover frames.The NLCA block aims to effectively produce the enhanced representations of the secret frames to enhance the robustness of the model and alleviate the influence of different areas in the secret video.Furthermore,the DRANet reduces the model parameters by performing similar operations on the different frames within an input video recursively.Experimental results show the proposed DRANet achieves better performance with fewer parameters than the state‐of‐the‐art competitors.

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。

视频侦查技术关键及其发展展望

视频侦查技术的核心是通过视频图像的提取、查看、分析和研判来获取侦查线索、固定涉案证据。多年来经过在实战应用中不断地发展创新,视频侦查技术形成了自己独特的关键技术体系:视频信息分析解读技术是侦查应用和证据固定的基础和核心;视频证据固定保全技术的规范是审判中心主义的客观要求,可以获取视频侦查记录报告、视频检验鉴定报告或视频数据关联报告;低质量视频图像的增强恢复技术专业性强,应用范围窄,技术成熟度高,然而它不断面临新的挑战。目前,视频数据的智能应用在大数据背景下变得越来越重要,仍需进一步突破视频自动识别技术的应用范畴,建立完善多层次的视频数据综合应用体系,打造适应不同业务需要的视频数据实战应用模型。

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。

涉Sora等文生视频危害行为的刑法规制

Sora等文生视频的应用将对刑法的法益理论产生重大影响。应将文生视频生成内容纳入作品范围。文生视频是其生成内容的作者但尚不是权利主体,应将相关著作财产权转移给使用人。涉文生视频强制猥亵、侮辱罪所侵害法益应当包括精神损害的内容。文生视频的应用改变了视频创作行业的社会关系,进而会改变相关犯罪行为法益侵害性程度的判断标准。涉文生视频犯罪主要涉及侵犯人身权利类犯罪、侵犯财产权利类犯罪以及寻衅滋事犯罪。司法解释对涉文生视频犯罪相关罪名构成要件的规定应当既有突破也有限制。我国刑法应设立数据安全责任事故罪以追究文生视频研发者、生产者的过失罪过下的刑事责任。应及时完善生成式人工智能产品应用的前置法规定。司法实践中应将文生视频的智能化程度区分为信息的整理、信息的基本加工、信息的深度加工三类,以此合理分配使用者、研发者与生产者所应当承担的涉文生视频犯罪的刑事责任。

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。

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。

决策树码率自适应算法的无数据蒸馏框架

码率自适应(Adaptive Bit-Rate,ABR)算法是流媒体视频传输中至关重要的技术.该算法根据当前网络情况和播放状态等因素,为下一个视频块选择合适的码率,以确保用户获得良好的体验质量(QoE).其中,基于学习的ABR算法因其不依赖传统模型和从头学习策略的特点,表现出良好的性能,并逐渐取代需要繁琐调优的启发式ABR算法,成为研究领域的热点.然而,这些算法使用神经网络推理,导致模型参数较多,整体计算量较大,使得在实际场景中难以部署.因此,以往的研究提出了决策树蒸馏方案,即使用轻量级的决策树来提取基于学习的ABR算法的专家策略,并在线上部署这些决策树.然而,本文的实验结果表明,过去的蒸馏框架忽略了训练环境对蒸馏后策略的影响,导致策略的泛化能力较差.因此,本文提出了一种名为NIA(data-free Network-environmental Imitationbased rate Adaptation framework)的新型无数据蒸馏框架,用于生成具有更好泛化性能的决策树A BR算法.NIA通过网络环境生成模块构建多个人工网络环境,并在每次迭代训练前使用环境选择模块来选择适合的网络场景,然后与该场景进行交互,利用基于学生驱动的模仿学习算法完成决策树的蒸馏过程.本文还设计了完整的评测平台测试NIA的性能.实验表明,NIA在各种带宽数据集上展现出良好的QoE性能和泛化性能:(1)相较于启发式算法,在QoE指标上提升了1%~46%;(2)与以往的决策树蒸馏方案相比,在低带宽场景下表现相当,但在高带宽场景下提升了近1倍;(3)总体性能接近甚至超过基于学习的算法(即专家策略)的表现.