Automatic Segmentation of Moving Objects in Video Sequences for Indoor and Outdoor Applications

1　IntroductionAutomaticsegmentationofmovingobjectsfromvideosequencesisadifficultandchallengingproblemincomputervisionsystems.Ithasmanyapplicationssuchasvideosurveillance,trafficmonitoring ,peopletrackingandvideocommunication[1～4] .Italsoplaysanimportantroleinsupportingcontent basedimagecoding,especiallyaftertheemergenceofthevideocodingstandardMPEG 4[5～ 1 4 ] .Therearealotofresearchworksonmovingob jectssegmentationandextraction .Thesealgorithmscanberoughlyclassifiedintotwocategories:inter …     

Time efficient ghost removal for motion detection in visual surveillance systems

Segmentation of foreground objects is an important and essential task for many systems that aim to carry out motion tracking, object classification, event detection and is used in applications such as traffic monitoring and analysis, access control to special areas, human and vehicle identification and the detection of anomalous behaviour. The most common approach for detecting moving objects is background subtraction, in which each frame of a video sequence is compared against a background model. A large number of background subtraction algorithms have been proposed [1], but problems remain for moving object identification under certain conditions. One of the toughest problems in background subtraction is caused by the detection of false objects when an object that belongs to the background (e.g. after staying stationary for some time) starts to move away. This generates what are called `ghosts?. It is important to address the problem because ghost objects will adversely affect many tasks such as object classification, tracking and event analysis (e.g. abandoned item detection). This Letter focuses on the problem of ghost identification and elimination. We used a state-of-the-art industrial tracker which includes basic background subtraction and object tracking. Then we included our ghost detection algorithm into the basic tracker to identify and eliminate ghosts. Finally, we systematically evaluated and compared performance on urban traffic video sequences.     

Detecting moving object from dynamic background video sequences via simulating heat conduction

Moving object detection is one of the essential tasks for surveillance video analysis. The dynamic background often composed by waving trees, rippling water or fountains, etc. in nature scene greatly interferes with the detection of moving objects in the form of noise. In this paper, a method simulating heat conduction is proposed to extract moving objects from dynamic background video sequences. Based on the visual background extractor (ViBe) with an adaptable distance threshold, we design a temperature field relying on the generated mask image to distinguish between the moving objects and the noise caused by dynamic background. In temperature field, a brighter pixel is associated with more energy. It will transfer a certain amount of energy to its neighboring darker pixels. Through multiple steps of energy transfer the noise regions loss more energy so that they become darker than the detected moving objects. After heat conduction, K-Means algorithm with the customized initial clustering centers is utilized to separate the moving objects from background. We test our method on many videos with dynamic background from public datasets. The results show that the proposed method is feasible and effective for moving object detection from dynamic background sequences.     

Intelligent Mobile Video Surveillance System with Multilevel Distillation

This paper proposes a mobile video surveillance system consisting of intelligent video analysis and mobile communication networking. This multilevel distillation approach helps mobile users monitor tremendous surveillance videos on demand through video streaming over mobile communication networks. The intelligent video analysis includes moving object detection/tracking and key frame selection which can browse useful video clips. The communication networking services, comprising video transcoding, multimedia messaging, and mobile video streaming, transmit surveillance information into mobile appliances. Moving object detection is achieved by background subtraction and particle filter tracking. Key frame selection, which aims to deliver an alarm to a mobile client using multimedia messaging service accompanied with an extracted clear frame, is reached by devising a weighted importance criterion considering object clarity and face appearance. Besides, a spatial-domain cascaded transcoder is developed to convert the filtered image sequence of detected objects into the mobile video streaming format. Experimental results show that the system can successfully detect all events of moving objects for a complex surveillance scene, choose very appropriate key frames for users, and transcode the images with a high power signal-to-noise ratio (PSNR).     

复杂背景下基于贝叶斯-全概率联合估计的前景检测

针对复杂背景下前景提取较为困难或者提取准确率较低等问题,该文提出了基于贝叶斯-全概率联合估计的目标检测模型并引入了背景误差控制变量的概念。通过选择适当的特征向量,在贝叶斯-全概率估计模型下,背景像素将会分为静止与运动两种不同的类型,进而准确提取前景像素点。实验结果表明,该模型是一个较为通用的目标检测模型,在目标提取时,该文算法对各种类型的视频背景环境(包括复杂背景)都具有较好的适用效果。     

Spatio-temporal segmentation for video surveillance

The automatic extraction of moving objects and construction of site models are key problems in video surveillance systems. A novel approach to segmenting moving objects from static scenes as well as acquiring background images automatically is presented. Experimental results on real video sequences demonstrate the robustness and accuracy of the algorithm     

基于嵌入式的新型运动目标视频检测算法

提出一类新型的嵌入式视频活动目标检测算法,该算法采用Surendra算法对背景进行更新以减低系统误报警的几率,对连续3帧图像分别采用差帧法,对2次帧差取交集实现对前景目标的模糊跟踪,而后对粗糙的运动区域图像进行阈值面积消去处理和数学形态学运算,最后实现目标定位跟踪。仿真结果表明,与传统的二阶帧差的方法相比,视频活动的目标检测算法具有高实效,高精度的特点。     

室内夜间微弱光源下运动目标检测和阴影去除

提出一种新的室内夜间微弱光源照明情况下的运动目标检测方法。首先进行背景建模, 获取稳固的背景图像, 之后对背景和当前帧图像进行图像增强处理, 提高其清晰度; 采用相对背景减法检测前景运动目标, 并对差分图像进行去噪和修补; 利用前景目标区域、阴影区域和背景区域像素亮度值存在差异的特点, 检测和去除背景差分图像中可能存在的阴影, 获得准确的运动目标。在室内夜间环境下采集视频进行试验, 结果验证了所提方法的有效性。     

基于HSV空间阴影去除方法研究与应用

在智能视频监控系统中,运动阴影如果被误判为运动目标,将会影响到场景中运动目标的准确提取、跟踪和预测。针对这一问题,设计了一种基于HSV颜色空间的阴影去除方法。方法首先将背景差法和三帧差分法相结合,用于提取运动目标,再将提取的含有阴影的运动目标区域映射到其HSV色彩空间,通过与背景和相邻帧的亮度、饱和度比较,实现对阴影区域的检测和去除,处理过程中无需提前确定特征判别参数。将所设计的方法在标准高速公路视频数据库中进行测试并应用于实时的视频监控系统,验证结果表明该方法能更加有效的消除阴影,从而准确的检测出运动目标,同时方法对光线变化具有一定的鲁棒性。     

基于背景重构的运动对象越界侦测方法

提出了一种基于背景重构的运动对象越界侦测方法,该算法利用当前视频图像和背景视频图像,通过差分法获取运动对象模型和背景差值,再利用运动对象模型的连续性绘制运动对象轨迹,对轨迹和警戒线交叉方程进行越界侦测。同时,利用当前视频图像融合背景图像生成新的背景图像,能有效保证识别算法的自适应性,提高侦测结果的准确性。     

Moving object detection in wavelet compressed video

In many surveillance systems the video is stored in wavelet compressed form. In this paper, an algorithm for moving object and region detection in video which is compressed using a wavelet transform (WT) is developed. The algorithm estimates the WT of the background scene from the WTs of the past image frames of the video. The WT of the current image is compared with the WT of the background and the moving objects are determined from the difference. The algorithm does not perform inverse WT to obtain the actual pixels of the current image nor the estimated background. This leads to a computationally efficient method and a system compared to the existing motion estimation methods.     

一种解决波动式干扰影响的序列图像运动目标检测方法

为解决复杂环境下的诸如枝叶摇摆、摄像机抖动等波动式干扰对运动目标检测的影响问题,该文提出基于视频窗口切分与分类的序列图像运动目标检测算法。首先将序列图像切分为rc大小的视频窗口,然后提取窗口内区域图像累积帧间差矩阵的简单统计特征,针对每一帧序列图像,将视频窗口进行分类,把它们划分为运动目标窗口和非运动目标窗口(包括静止背景窗口和波动式干扰窗口),最后将运动目标窗口合并为运动目标。该方法的优点是无需已知背景模型和运动目标大小、形状等任何先验信息。实验表明该算法能在摄像机抖动以及枝叶干扰等复杂环境下快速有效的检测出运动目标。     

基于背景差分的一种运动目标检测方法

针对运动目标检测中,传统背景差分法在运动目标和背景颜色相近时不易检测的缺点,提出了一种检测完整运动目标的方法。该方法对YUV彩色空间下的3个通道分别选取独立的阈值进行初次检测,最大化地利用了视频中图像的色彩信息。在包含初次检测所获运动目标的最小矩形区域内进行二次检测,有效地提高了检测精度。实验证明,相比于常规方法,该方法的检测结果更加清晰完整。     

一种基于梯度图像帧间差分和背景差分的运动目标检测新方法

提出一种基于梯度图像,融合帧间差分和背景差分的运动目标检测新方法.其特点是采用混合高斯背景模型,先利用针对梯度图像的帧间差分检出变化区域,再利用背景差分从变化区域中检出运动物体,最后利用连通性检验消去噪声和阴影.针对真实视频序列的实验结果表明,该方法既简单有效,又具有较小的运算量和较好的鲁棒性.     

基于鲁棒主成分分析的运动目标检测优化算法

针对鲁棒主成分分析(Robust Principal Component Analysis, RPCA)算法中将动态背景误检为运动目标的问题,该文提出一种运动目标检测优化算法。在RPCA算法初步检测出运动目标后,利用动态背景在时间域上满足高斯分布的特性,以及动态背景和运动目标在整个视频流上检出点均值和方差的差异特性,进一步将动态背景和运动目标分离开来。实验结果表明,所提算法能够有效地处理动态背景的问题,并在一定程度上完整检测出运动目标。     

Maritime filtering for images and videos

Maritime signal processing technologies have emerged as an important area of study because of the increasing popularity of autonomous ships and automatic maritime surveillance systems. However, the various techniques developed for detecting or tracking objects remain unable to address various maritime noise challenges that cause several types of false positives in maritime visual surveillance. Maritime signal processing is challenging because of the prevalence of noise sources such as severe dynamic backgrounds, wakes, and reflections, owing to the complex, unconstrained, and diverse nature of such scenes caused by the surface properties of water. Moreover, few studies have investigated specific maritime noise filtering as a general integrated processing approach with image and video technologies in the context of maritime visual surveillance. In this study, we propose a novel maritime noise prior (MNP) based on a dark channel prior and observations of the characteristics of the sea. A general maritime filtering technique is developed to suppress noise originating from the properties of water in maritime images and videos. The proposed method employs a noniterative, nonlinear, and simple maritime filtering approach based on MNP that does not require specialized knowledge of application scene conditions or structure. We conducted image and video experiments by applying our approach to three publicly available databases. In experiments with color images, our method successfully filtered related background noise and water, i.e., severe boat wakes and reflections, while preserving objects other than water in color images. In the experiments with video sequences, the results demonstrated that the proposed filter improved the overall performance of state-of-the-art background subtraction (BS) algorithms from 36.60%–50.63%. By combining BS algorithms and filtering to enhance foreground detection in video sequences, the proposed method ensures the universal applicability and flexibility required to eliminate noise from images and videos obtained in challenging maritime environments. The results indicate that the proposed method is appropriate for maritime surveillance applications implementing image segmentation and foreground detection, and it can potentially increase the accuracy of maritime visual surveillance.     

A simulation based approach for network resources dimensioning in video delivery systems

The availability of high-performance networking technologies and processing power allows creation of new multimedia services and applications in broadband integrated services networks. Such new services will be successful only if delivered to end-users at low cost. At present, for wide-area distributed systems, communication has a relevant impact on the overall costs. As a consequence, adequate methodologies for an efficient dimensioning of the network resources need to be devised. Simulation techniques represent a powerful instrument for the analysis and evaluation of real systems. This paper, moving from the definition of mathematical models for VBR video sources, presents the application of a simulation approach to verify the compatibility of the communication load related to a set of video streams with the data transport capability of an existing network architecture for the delivery of video streams, in the case of CBR communication links.     

一种新的自适应背景建立与更新算法

针对视频监控系统中的动目标检测问题,提出了一种新的自适应的背景建立与更新算法。能够从含有运动目标的初始背景中,通过短时间的训练得到理想的背景,可以大大减轻系统的存储负担。实验表明算法能很好地适应光照的缓变和突变,具有较强的鲁棒性。     

基于改进surendra背景更新算法的运动目标检测算法

提出了一种改进的surendra运动目标检测算法,该算法可以自适应的调整背景更新速度。首先将第一帧图像作为背景图像,并利用改进的surendra背景更新算法根据每帧图像对背景图像进行更新获得可靠的背景。然后,将当前帧与背号作差,得到差值图像。使用自适应阈值对差值图像进行二值化处理,并利用形态学滤波对二值图像进行适当处理,这样运动目标就被准确地提取出来。     

融合颜色信息与深度信息的运动目标检测方法

基于颜色信息的运动目标检测易受光照、阴影等影响,基于深度信息的运动目标检测存在目标边缘噪声大,无法检测距离背景较近的目标等问题。针对上述问题,该文利用CCD相机获取的颜色信息及TOF相机获取的深度信息分别为每个像素建立颜色与深度信息的分类器,根据像素点的深度特征及前一帧的检测结果,自适应地为每个分类器的输出分配不同的权值,实现运动目标的检测。该文采集多组视频序列进行实验,实验结果表明该方法能有效解决单独利用颜色或深度信息进行运动目标检测时出现的问题。