视频监控系统概述

综合介绍了视频监控系统的发展、结构组成和功能应用等方面的内容，并对不同情况下的视频运动检测算法进行了介绍。在视频监控系统中对图像序列进行运动检测，可以大大提高监控系统的功能，促进监控系统的智能化发展。     

基于LINUX与H264的安全视频监控系统

采用DSP TMS320C6711和ARM920T,设计并实现了支持H.264加密的安全视频监控系统.提出了一种将高级加密标准AES与H.264相结合的安全视频加密系统设计方案,并在ARM-Linux嵌入式平台下予以实现.解决了视频监控系统中视频数据保密等问题,并给出了改进加密算法的实验结果.     

用ARM9实现移动目标跟踪的视频监控系统

提出了一种基于ARM的无线视频监控系统,该监控系统使用S3C2440处理器作为主控制器,并使用UVC摄像头进行视频数据的采集,最后利用WiFi网卡发送视频数据.搭载Linux操作系统并利用Mjpg_streamer视频服务器发送视频数据,然后通过自己设计的客户端接收视频数据.最后在基于Mean-shift算法的基础之上,使用Opencv 2.4.12和VS 2010开发平台以及C/C++编程语言实现了运动目标跟踪.实验结果表明,该监控系统可以为用户提供实时的 、稳定的 、清晰的视频图像,并且成功实现了在运动背景不太复杂,运动目标慢速移动情况下的运动目标的跟踪.     

一种基于视频编码标准H.264的智能视频监控技术

提出并实现了一种基于H.264视频编码标准的智能视频监控系统。该系统引入一种新的运动检测方法,通过对监控图像的运动矢量及运动补偿后的残差能量进行综合分析,提出了对可疑场景的确认和报警方案,能够有效克服光线变化、背景局部轻微摆动及摄像头轻微抖动带来的影响,具有良好的灵敏度和鲁棒性。     

监控视频聚簇模式挖掘及其应用

在监控视频中,新输入的视频帧与可更新背景的差异可以实时反映监控场景中运动目标的大小和多少等信息.本文以此计算帧运动量,提出一种监控视频场景聚簇模式挖掘方法.先依据帧运动量的变化分割监控视频流,然后计算视频段的平均运动量,并对其进行K均值聚类分组,最后利用获得的聚簇知识,可以对监控视频实现多尺度摘要和相似视频检索.     

自然保护区海量视频数据快速分析与检索

近年来,随着视频监控系统在自然保护区的大量部署,如何有效利用日益增加的海量视频监控数据成为亟待解决的难题.通过基于图像相似度的关键帧提取算法对海量视频数据进行清洗和压缩,同时利用基于深度学习的目标检测算法提取关键帧中的有效视频信息,并提供多种基于内容的视频检索方式,自动对用户提交的检索内容进行分析与处理,从而快速检索出感兴趣的视频.通过对青海湖野生动物视频监控数据进行分析与检索,验证了该系统的有效性.     

基于运动目标的监控视频检索方法

随着我国视频监控网络的不断壮大,监控视频数据呈现海量增长的趋势,如何快速有效地进行监控视频检索变得越来越重要。提出一种基于运动目标的监控视频检索方法。对监控视频中的运动目标提取特征,生成相应的视频描述文件,通过匹配查询图片与描述文件中的运动目标特征最终找到相似对象出现的视频片断。实验表明,该方法实用性较好。     

聚类挖掘在监控视频中的应用

提出了一种针对监控视频数据的挖掘算法.算法根据输入视频帧与可更新背景帧的差异计算帧运动量,按运动量所属类型分割视频段,然后根据视频段的平均运动量采用K均值算法进行聚类分组.通过对大楼走廊的监控视频进行运动量挖掘,能对可能的异常事件给予告警,发现一天中各时段走廊的拥挤程度和模式.实验结果表明该挖掘算法对于室内走廊监控视频是有效的和鲁棒的,可以在一定程度上提高监控视频的智能应用.     

基于内容查询的智能安全监控视频数据库系统研究

文章研究了基于内容的智能监控视频数据库的系统结构,并提出了相应的视频数据模型,并将视频分成背景图像和运动图像分别存储入库,使智能监控数据库的数据压缩比得到质的提高。     

视频监控系统中运动检测算法的研究

运动检测是现代视频监控系统的核心.在分析和探讨了几种运动检测算法后,提出了一种新的应用于视频监控系统的运动检测算法,该算法结合统计法和边缘检测筛选出运动边缘点,对运动边缘点逐个进行块匹配最优搜索,估算出运动矢量.该算法的应用有效地压缩了存储的视频数据,改善了系统性能,有很好的实用价值.     

Video data mining: semantic indexing and event detection from the association perspective

Advances in the media and entertainment industries, including streaming audio and digital TV, present new challenges for managing and accessing large audio-visual collections. Current content management systems support retrieval using low-level features, such as motion, color, and texture. However, low-level features often have little meaning for naive users, who much prefer to identify content using high-level semantics or concepts. This creates a gap between systems and their users that must be bridged for these systems to be used effectively. To this end, in this paper, we first present a knowledge-based video indexing and content management framework for domain specific videos (using basketball video as an example). We will provide a solution to explore video knowledge by mining associations from video data. The explicit definitions and evaluation measures (e.g., temporal support and confidence) for video associations are proposed by integrating the distinct feature of video data. Our approach uses video processing techniques to find visual and audio cues (e.g., court field, camera motion activities, and applause), introduces multilevel sequential association mining to explore associations among the audio and visual cues, classifies the associations by assigning each of them with a class label, and uses their appearances in the video to construct video indices. Our experimental results demonstrate the performance of the proposed approach.     

视频结构挖掘的概念及应用

提出了一种视频结构挖掘的概念框架和视频结构挖掘系统框架,在概念框架中对视频结构挖掘相关概念给出了规范化的定义,视频结构挖掘框架包括的主要内容有视频基本结构挖掘、视频语法结构挖掘和视频语义结构挖掘。最后讨论了视频结构挖掘中发现的结构模式和知识的具体应用,包括指导视频的组织与管理、实现基于内容的个性视频推荐和改善视频摘要系统。     

3-D–2-D spatiotemporal registration for sports motion analysis

Computer systems are increasingly being used for sports training. Existing sports training systems either require expensive 3-D motion capture systems or do not provide intelligent analysis of user’s sports motion. This paper presents a framework for affordable and intelligent sports training systems for general users. The user is assumed to perform the same type of sport motion as an expert, and therefore the performer’s motion is more or less similar to the expert’s reference motion. The performer’s motion is recorded by a single stationary camera, and the expert’s 3-D reference motion is captured only once by a commercial motion capture system. Under such assumptions, sports motion analysis is formulated as a 3-D–2-D spatiotemporal motion registration problem. A novel algorithm is developed to perform spatiotemporal registration of the expert’s 3-D reference motion and a performer’s 2-D input video, thereby computing the deviation of the performer’s motion from the expert’s motion. The algorithm can effectively handle ambiguous situations in a single video such as depth ambiguity of body parts and partial occlusion. Test results on Taichi and golf swing motion show that, despite using only single video, the algorithm can compute 3-D posture errors that reflect the performer’s actual motion error.     

视频结构挖掘的概念和框架

视频信息处理的关键是视频信息的结构化，视频除了有基本层次结构之外，还有隐藏其中的视频结构语法和结构语义。该文提出了一种视频结构挖掘的概念框架和视频结构挖掘的系统框架，在概念框架中对视频结构挖掘相关概念给出了明确定义和界定；视频结构挖掘框架主要包括：视频基本层次结构挖掘，视频结构语法挖掘和视频结构语义挖掘。讨论了视频结构模式和知识的具体应用，包括指导视频的组织与管理、实现基于内容的个性视频推荐和改善视频摘要系统。     

一种基于运动的视频层次化分析框架

运动信息是视频中最重要的特征之一.本文提出了一种新颖的基于运动的视频层次化分析框架.为了揭示视频流中的运动关系和在拍摄过程摄像机的运动信息即拍摄者的兴趣所在,提出了视频层次化结构来对视频流进行描述和检索.该运动层次化结构引入一个新的概念:基本运动单元(Basic Motion Unit,BMU).BMU反映的是MPEG-7中定义的基本摄像机操作,这样就可以把视频分割成BMU的集合.在BMU的基础上发现并挖掘出视频流中潜在的运动语义模式,最后根据运动活力(Motion Activity)将BMU聚类为运动节奏快慢的运动活力单元(Motion Activity Unit,MAU).该基于运动的视频分析框架不仅能够直观地捕获视频流在拍摄过程中拍摄者的注意力,而且为视频流的快速浏览和检索提供了新的思路.     

Classifying spatiotemporal object trajectories using unsupervised learning in the coefficient feature space

This paper proposes a novel technique for clustering and classification of object trajectory-based video motion clips using spatiotemporal function approximations. Assuming the clusters of trajectory points are distributed normally in the coefficient feature space, we propose a Mahalanobis classifier for the detection of anomalous trajectories. Motion trajectories are considered as time series and modelled using orthogonal basis function representations. We have compared three different function approximations – least squares polynomials, Chebyshev polynomials and Fourier series obtained by Discrete Fourier Transform (DFT). Trajectory clustering is then carried out in the chosen coefficient feature space to discover patterns of similar object motions. The coefficients of the basis functions are used as input feature vectors to a Self- Organising Map which can learn similarities between object trajectories in an unsupervised manner. Encoding trajectories in this way leads to efficiency gains over existing approaches that use discrete point-based flow vectors to represent the whole trajectory. Our proposed techniques are validated on three different datasets – Australian sign language, hand-labelled object trajectories from video surveillance footage and real-time tracking data obtained in the laboratory. Applications to event detection and motion data mining for multimedia video surveillance systems are envisaged.     

Motion sickness, console video games, and head-mounted displays

OBJECTIVE: We evaluated the nauseogenic properties of commercial console video games (i.e., games that are sold to the public) when presented through a head-mounted display. BACKGROUND: Anecdotal reports suggest that motion sickness may occur among players of contemporary commercial console video games. METHODS: Participants played standard console video games using an Xbox game system. We varied the participants' posture (standing vs. sitting) and the game (two Xbox games). Participants played for up to 50 min and were asked to discontinue if they experienced any symptoms of motion sickness. RESULTS: Sickness occurred in all conditions, but it was more common during standing. During seated play there were significant differences in head motion between sick and well participants before the onset of motion sickness. CONCLUSION: The results indicate that commercial console video game systems can induce motion sickness when presented via a head-mounted display and support the hypothesis that motion sickness is preceded by instability in the control of seated posture. APPLICATION: Potential applications of this research include changes in the design of console video games and recommendations for how such systems should be used.     

一种基于WebRTC的网络遥操作系统

遥操作系统能够使人类在危险或者复杂的工业场景中进行实时操作.在当前的遥操作系统中,操作端的视频质量对于操作者能否正常完成工作起着决定性的作用,而实时的视频质量又会受到带宽受限的网络环境的影响,且不能实现便捷的移动设备的访问.为此,设计了一种基于WebRTC （Web with Real Time Communications）的网络遥操作系统,利用WebRTC技术来实现远端到操作端的实时音视频传输,并且利用Websocket来传输操作端的控制指令,并且实现了远端移动平台的运动规划.最后的实验表明该系统能够有效降低传输速率,提高视频质量.     

视像中的摄像机运动检测

摄像机的运动信息是视像内容检索中的一种重要特征。文中介绍了两种基于全局运动的摄像机运动检测方案。首先提取视像中的运动特征作为原始数据,然后分别使用了迭代最小二乘法和支持向量机分类的方法进行摄像机运动的检测,通过对试验结果的对比,前一种方案可更有效地完成摄像机运动类型的判断。     

一种适于实时应用的快速有效的自适应运动估计算法

运动估计是视频编码系统的关键部分,同时也是整个视频编码器中计算量最大的部分,运动估计性能的优劣直接影响到整个视频编码器的运行效率和整个视频序列的重构质量。该文提出了一种适用于实时应用的快速自适应运动估计算法,该算法首先利用运动矢量时空域的相关性预测初始运动矢量,然后对不同运动程度的图像块进行不同大小的菱形搜索,同时采用了高效的提前截止准则。实验结果表明,对于各种不同运动程度的视频序列,该文算法都可以获得和全搜索法基本相同的图像质量,同时可以节省超过99%的计算量,该文算法在搜索速度和搜索精度两方面比快速算法中性能突出的菱形搜索具有更大的优势,更适合实时应用。