视频监控中背景的提取和更新算法

获取高质量的背景图像是在背景消减法中决定目标提取效果的关键。针对传统方法存在的问题,从实际情况出发提出一种改进的背景提取和更新算法,并在Matlab环境下进行仿真,仿真结果显示该算法具有良好的性能,说明它对于各种外部因素的影响,如光照变化、阴影、目标遮挡等,具有很强的适应能力。     

基于差分图像分块的视频背景提取算法

为了降低背景提取算法的时间复杂度和空间复杂度,提出一种结合差分图像分块、背景减除和帧间差法的背景提取方法。对差分图像进行分块分类,提出了一种统计像素值的子块分类法,对不同类的块用不同的更新策略进行背景实时更新。该算法有效解决了背景更新过程中运动目标逗留、背景物体移入移出等问题的影响。实验结果表明该算法运算速度快、鲁棒性高、能准确地提取实时背景。     

一种基于高斯混合模型的分块背景更新算法研究

随着经济建设的快速发展,城市交通问题成为了人们亟待解决的问题。在智能交通系统领域中,背景提取和背景更新成为近年来计算机视觉领域的研究热点[1]。提出了一种应用于智能视频监控中的背景更新方法。在视频图像中,通过利用高斯混合模型获取背景图像,通过分块背景更新的方式提高背景更新的速度。     

谈谈智能视频监控

随着计算机视觉等相关技术的发展，尤其是“平安城市”等项目的启动，海量视频的处理需求随之产生，借此机会，智能视频监控开始崭露头角，成为安防界的热门话题之一。[编者按]     

如何有效实现智能视频监控

传统的监控已经越来越难以满足用户的需求，而主动的智能视频监控已经正式走向了市场，并逐渐的被用户认可和接受。目前有很多用户已经开始尝试使用智能视频监控，但实际的效果与预期还是有很大的差距，那么怎么才能有效的实现智能视频监控呢？     

基于分块背景更新的均值飘移跟踪算法

为提高融合背景信息均值飘移跟踪算法中的背景模型更新效率,提出基于分块背景更新的改进算法。将目标周围的背景区域划分成4块,分别以此4个子块的颜色直方图作为区域特征描述符进行计算,利用巴氏系数选择性地更新相似性较低的3个区域。在构建目标候选模型时,利用目标物体空间信息赋予候选目标区域像素点相应的距离权重。对整块背景信息更新策略和加权更新背景信息策略进行比较,比较结果表明,该算法具有更高的迭代收敛效率和更强的抗背景干扰能力。     

智能视频监控系统设计

针对目前智能视频控制系统只是对运动目标进行识别跟踪，拍摄范围比较大，取证利用价值较低的缺点，设计了一套基于人脸的智能视频控制系统．完成了云台控制系统的软件及硬件设计。本系统就是在对运动目标进行识别基础上进行人脸检测，把人脸放在屏幕中央，达到抓证取证目的，提高了利用价值。     

一种有效的道路背景提取与更新算法

针对复杂环境中道路背景图像的快速获取问题,提出了一种快速有效的道路背景提取和更新算法。应用改进的多帧平均算法提取背景,采用改进的Surendra算法对背景进行更新。实验结果表明,该算法能够减轻初始静止车辆对背景建立的影响,能及时消除由于初始帧中目标移动而造成的鬼影,对光线变化鲁棒性高、速度快、更新效果好。     

智能视频监控软件的演进

一系列引人注目的新技术正在进入智能监控市场，通过与独特环境算法的结合，这些解决方案已经成为现实。本文关注的正是这一日益扩展的视频软件市场的未来，最新的解决方案，以及即将出现的应用领域。[编者按]     

智能视频监控和智能视频分析技术在检察工作中的应用

介绍了智能视频监控和智能视频分析的基本概念、工作原理及相关技术。分析了当前"监视居住"在司法实践中存在的问题。借鉴智能视频监控技术和智能视频分析技术的成熟应用经验,将该技术应用到"监视居住"中。利用智能视频监控技术和智能视频分析技术实现智能检察,为落实检察机关科技强检战略开辟了一条新思路。     

智能视频监控系统中改进YOLO算法的实现

智能视频监控主要采用了人工智能、图像处理等先进技术对监控摄像头获得的图像进行分析和处理,在各行各业中得到了广泛的应用,成为当今社会研究的热点。介绍了智能视频监控系统的架构,针对数字化车间的特点,提出一套基于深度学习的检测方法,将YOLO检测算法用于数字化车间中人员和安全帽的检测。为了提高YOLO算法检测小物体的能力,设计了一种改进的YOLO网络结构,将RPN检测算法融入到YOLO算法中,并借鉴R-FCN算法,去掉一个全连接层,在卷积层上进行滑动窗口操作,采用先池化再卷积的方法,以减少图片特征丢失。将改进后的算法用于视频监控中,有效地减少了检测过程中图片特征的丢失,系统检测的实时性和准确率均满足实际工程的需求,实现了车间的智能检测。     

基于Android平台的车载视频智能监控系统的研究

针对传统车载视频监控系统网络资源利用率低、高清实时性能较差的问题,提出了一种基于Android平台的车载视频监控解决方案,对系统中关键模块作了重点研究。系统实现了P2P和C/S混合网络架构、多线程机制、丢包和包乱序处理,从而提高了实时监控性能。经实验证明,该系统实现了针对车辆的实时高效的监控,利于向智能交通领域中推广。     

基于云计算的智能高清视频监控系统研究

网络视频监控系统对海量视频数据存储和智能视频分析服务的需求日益突出,针对系统硬件计算能力有限,不能满足用户需求的多样性,系统扩展性受到存储设备容量的限制等问题,通过虚拟化技术构建云计算基础资源池,以Hadoop分布式文件系统存储海量视频数据,使用MapReduce编程模型实现大规模并行计算,提高了系统的可扩展性和可靠性,为用户提供实时视频监控和智能视频分析服务.     

An improved target tracking algorithm and its application in intelligent video surveillance system

Multimedia Tools and Applications - Target tracking is one of the pivotal technologies in intelligent video surveillance systems. Facing the complex and various scenarios in practical applications,...     

Multiclass object classification for real-time video surveillance systems

Object classification in video is an important factor for improving the reliability of various automatic applications in video surveillance systems, as well as a fundamental feature for advanced applications, such as scene understanding. Despite extensive research, existing methods exhibit relatively moderate classification accuracy when tested on a large variety of real-world scenarios, or do not obey the real-time constraints of video surveillance systems. Moreover, their performance is further degraded in multi-class classification problems. We explore multi-class object classification for real-time video surveillance systems and propose an approach for classifying objects in both low and high resolution images (human height varies from a few to tens of pixels) in varied real-world scenarios. Firstly, we present several features that jointly leverage the distinction between various classes. Secondly, we provide a feature-selection procedure based on entropy gain, which screens out superfluous features. Experiments, using various classification techniques, were performed on a large and varied database consisting of ∼29,000 object instances extracted from 140 different real-world indoor and outdoor, near-field and far-field scenes having various camera viewpoints, which capture a large variety of object appearances under real-world environmental conditions. The insight raised from the experiments is threefold: the efficiency of our feature set in discriminating between classes, the performance improvement when using the feature selection method, and the high classification accuracy obtained on our real-time system on both DSP (TMS320C6415-6E3, 600 MHz) and PC (Quad Core Intel® Xeon® E5310, 2 × 4 MB Cache, 1.60 GHz, 1066 MHz) platforms.     

基于Web的远程视频监控系统的设计

本文给出了一种采用B/S模式的嵌入式视频监控系统,能够实现web方式的远程监控。通过构建嵌入式流媒体服务器实现视频的处理和传输;然后搭建嵌入式Web服务器,实现基于Web方式的远程视频监控。本设计有很强的实时性,交互性较好,方便管理和控制。     

A new video watermarking algorithm based on shot segmentation and block classification

In this paper, we propose a new video watermarking algorithm based on shot segmentation and block classification to enhance the robustness, imperceptibility and real-time performance based on the H.264/AVC codec. A method of selecting host frames is proposed based on shot segmentation to avoid embedding watermark frame by frame, so as to improve the robustness and the real-time performance. The watermark signal is cropped into small watermarks according to the number of shots in the host video, and small watermarks are respectively embedded into different shots. The watermarking capacity and the perceptual quality are greatly improved by this way. A method of selecting host coefficients is proposed based on block classification in the Discrete Cosine Transformation (DCT) compressed domain. The texture characteristics of host blocks are considered in the classification and the places of host coefficients can change adaptively according to the content of the video. The imperceptibility of the watermarked video is greatly improved by this way. The simplified quantization index modulation (QIM) is applied to embed watermark. It brings fewer artifacts to the host signal than the current main watermarking method, such as spread spectrum (SS), differential energy watermarking (DEW) and so on. The experiment results show that the proposed scheme has a good performance in maintaining real-time performance and resisting Gaussian noising, frame swapping, MPEG compression, etc.     

Scalable and flexible wireless distributed architecture for intelligent video surveillance systems

Multimedia Tools and Applications - This paper presents a novel distributed intelligent video surveillance architecture based on Wireless Multimedia Sensor Networks (WMSNs). This architecture is...     

A new robust video watermarking algorithm based on SURF features and block classification

In this paper, we propose a robust block classification based semi-blind video watermarking algorithm using visual cryptography and SURF (Speed-Up Robust Features) features to enhance the robustness, stability, imperceptibility and real-time performance. A method of selecting the best frames in each shot and the best regions or blocks within best frames is proposed to avoid employing frame–by-frame method for generating owner’s share in order to enhance robustness as well as reducing time complexity. In our method, Owner’s share is generated using the classification of selected robust blocks within the chosen frames along with corresponding watermark information. In extraction process, the SURF features are employed to match the feature points of selected frames with all frames to detect selected frames. Moreover, we resynchronize the embedded regions from distorted video to original sequence using SURF feature points matching. Afterwards, based on these matched feature points, rotation and scaling parameters are estimated next, selected blocks are retrieved using side information being stored eventually, watermark information is reconstructed successfully. Selecting Best frames, best regions, and employing surf features make our method to be highly robust against various kinds of attacks including image processing attacks, geometrical attacks and temporal attacks. Experimental results confirm the superiority of our scheme in case of being applicable in the real world, enhancing robustness and exploiting idea imperceptibility, over previous related methods.