Real-time detection of moving objects in video sequences

1 .INTRODUCTIONThe efficient detection of moving objects in video se-quences is a challenging and an i mportant task incomputer vision. It has many applications such assurveillance , video conference , traffic monitor-ing[1 ,2]. Traditionally, the most i mportant task ofsurveillance systemis based on human visual observa-tion,whichis a hard workfor watchmen.Therefore ,the automatic detection of moving objects is requiredinthe surveillance system. Tofacilitate a surveillancesystem,efficien…     

视频图像中运动目标的实时检测

提出了一种运动目标检测与跟踪算法。以每点色彩信息的混合高斯模型来实现对视频图像的背景估计,从而实现对运动目标的检测。利用模板匹配的方法实现对运动目标的跟踪,并对混合高斯模型的算法进行了改进,使其能更快、更有效地适应环境的变化。实验结果表明此方法具有较强的鲁棒性,能较好地适应各种气候和光照环境的变化。     

基于HSV颜色空间和码本模型的运动目标检测

码本模型的思想就是根据像素的颜色失真程度和亮度范围将背景像素值量化后用码本表示,然后利用减背景思想对新输入像素值与其对应像素码本做比较判断,从而提取出前景运动目标像素。提出将像素从RGB空间转换到HSV空间来计算颜色失真度,多种像素亮度和颜色失真度计算方法的对比实验结果表明,该方法能取得更好的目标检测效果。     

High efficient moving object extraction and classification in traffic video surveillance

Moving object extraction and classification are important problems in automated video surveillance systems. A background model based on region segmentation is proposed. An adaptive single Gaussian background model is used in the stable region with gradual changes, and a nonparametric model is used in the variable region with jumping changes. A generalized agglomerative scheme is used to merge the pixels in the variable region and fill in the small interspaces. A two-threshold sequential algorithmic scheme is used to group the background samples of the variable region into distinct Gaussian distributions to accelerate the kernel density computation speed of the nonparametric model. In the feature-based object classification phase, the surveillance scene is first partitioned according to the road boundaries of different traffic directions and then re-segmented according to their scene localities. The method improves the discriminability of the features in each partition. AdaBoost method is applied to evaluate the relative importance of the features in each partition respectively and distinguish whether an object is a vehicle, a single human, a human group, or a bike. Experimental results show that the proposed method achieves higher performance in comparison with the existing method.     

基于局部二元图的视频对象阴影检测方法

针对在视频对象分割时,运动投影常被误分为视频对象,给出一种新的视频阴影检测方法,该算法基于在灰度图像中阴影区域和背景相应位置具有相同纹理这一事实,其中利用自适应高斯混合模型进行背景建模,利用局部二元图(local binary patterns,LBP)来表征纹理。首先,进行基于自适应高斯混合模型的背景提取,获得包含运动投影的前景分割,分割时加入了LBP纹理相似性判断,减少了分割出的目标内的孔洞提高了分割的精确度。然后利用阴影区域和已获取背景相应位置的LBP纹理相似性,可较好的对视频阴影进行检测。通过实验,获得了不错的阴影检测实验结果,可较好地应用于运动目标检测分割及跟踪等领域。     

Redundant discrete wavelet transforms based moving object recognition and tracking

A method for moving object recognition and tracking in the intelligent traffic monitoring system is presented. For the shortcomings and deficiencies of the frame-subtraction method, a redundant discrete wavelet transform (RDWT) based moving object recognition algorithm is put forward, which directly detects moving objects in the redundant discrete wavelet transform domain. An improved adaptive mean-shift algorithm is used to track the moving object in the follow up frames. Experimental results show that the algorithm can effectively extract the moving object, even though the object is similar to the background, and the results are better than the traditional frame-subtraction method. The object tracking is accurate without the impact of changes in the size of the object. Therefore the algorithm has a certain practical value and prospect.     

Compressive sensing for small moving space object detection in astronomical images

It is known that detecting small moving objects in astronomical image sequences is a significant research problem in space surveillance.The new theory,compressive sensing,provides a very easy and computationally cheap coding scheme for onboard astronomical remote sensing.An algorithm for small moving space object detection and localization is proposed.The algorithm determines the measurements of objects by comparing the difference between the measurements of the current image and the measurements of the background scene.In contrast to reconstruct the whole image,only a foreground image is reconstructed,which will lead to an effective computational performance,and a high level of localization accuracy is achieved.Experiments and analysis are provided to show the performance of the proposed approach on detection and localization.     

基于背景差的码头运动船只检测

通过背景差法对码头视频图像中的运动船只检测。采用混合高斯模型为码头背景建模,该模型可克服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。对采用全局固定阈值进行运动目标分割的方法作了优化,并对运动船只目标进行分割。经现场测试,能有效地监测运动船只、记录异常事件以及视频图像的提取和过滤,提高了码头监管的质量和效率,可广泛应用于码头无人监控。     

运动小目标检测最优图像帧数概率模型

针对运动小目标轨迹检测方法存在的最长轨迹拟合范围难以确定的问题,研究了轨迹长度与小目标检测概率分布之间的可描述性和适用性,分析了基于二项式概率分布的轨迹长度优化模型参数与最优轨迹模型帧数之间的关系,连续轨迹中丟失图像帧数与预测误差之间的关系,以及丟帧引起的轨迹拟合长度有效性限制问题,建立了小目标轨迹检测最优帧数概率模型。利用不同干扰下的40组紫外小目标连续视频,进行轨迹建立帧数、轨迹跟踪帧数、连续丟帧有效性测试,验证了文中所建立模型的有效性。     

基于Faster R-CNN的航母舰面多尺度目标检测算法

针对航母舰面复杂的多尺度目标检测环境,且现有算法对牵引车、人员等小目标检测性能不佳的问题,提出一种改进快速区域卷积神经网络(faster region convolutional neural netuorks,Faster R-CNN)的舰面多尺度目标检测算法.基于多尺度特征层提取了不同尺度的区域建议网络,提高了算法...     

控制系统故障检测与诊断技术的最新进展

对故障检测与诊断(FDD)技术在控制领域的最新进展进行了综述,内容主要包括三类基本方法,即:基于解析模型的FDD、基于信号处理和基于知识的故障检测与诊断方法。重点评述了近年来发展起来的信息融合方法、多Agent方法、鲁棒故障检测、网络控制系统的故障诊断等。最后介绍了故障检测与诊断技术在控制系统中的若干应用成果,并指出尚待解决的主要问题和值得进一步研究的方向。     

基于模糊函数构型的动目标探测波形设计

针对工程中常规雷达波形存在距离旁瓣高、抗截获性差、多普勒容许性差等问题,本文借鉴噪声波形与线性调频(linear frequency modulation, LFM)波形联合思路来探测非合作动目标,即约束波形恒模条件下,利用相位尺度因子来考虑LFM波形与噪声波形的相位部分,构造恒模化的LFM-噪声复合波形;进而在模糊函数图及主优化算法(majorization-minimization, MM)体制下利用先验知识映射模板实现对特定距离-多普勒区间的波形优化,将该二维旁瓣抑制问题分步转化为多维求解问题,通过选取两次逼近函数来逼近原优化问题的解域,并在算法的每一次迭代中加入级联松弛-缩放步骤、相位校正步骤,使之加快算法迭代收敛效率,获得波形兼具低旁瓣、低截获及多普勒容许性。仿真表明:所提基于相位修正的松弛加权MM复合波形设计算法收敛迅速,相比常规算法具有明显的性能优势。     

Method of moving target detection based on sub-image cancellation for single-antenna airborne synthetic aperture radar

The method of moving target detection based on subimage cancellation for single-antenna airborne SAR is presented.First the subimage is obtained through frequency processing is pointed out.The imaging difference of a stationary objects and moving object in the subimage based on the frequency division is analyzed from the fundamental principle.Then the developed method combines the shear averaging algorithm to focus on the moving target in the subimage,after the clutter suppression and the focusing position in each subimage is obtained.Next the observation model and the relative movement of the moving targets between the subimages estimate the moving targets.The theoretical analysis and simulation results demonstrate that the method is effective and can not only detect the moving targets,but also estimate their motion parameters precisely.     

存在强旁瓣目标的机载雷达地面运动目标检测

针对机载雷达进行地面运动目标检测时不可避免地要面对一些强旁瓣目标的情况下,提出了一种在强旁瓣目标环境下的运动目标检测算法,该算法在估计出强旁瓣目标频率的基础上利用最大似然方法估计运动目标的幅度来进行检测,可以有效地克服强旁瓣目标对地面目标检测的影响。给出了应用该方法的具体步骤,通过仿真实验证明了该算法的有效性。     

基于决策融合的多无人机协同目标检测识别算法

针对现阶段单无人机不能高效完成大区域巡视的问题,提出一种多无人机决策融合的目标检测识别算法。首先改进Retinanet算法进行单无人机的目标检测,根据航拍图像目标特性,调整anchor参数和训练策略。同时利用特征提取算子配准多无人机航拍图像,实现多机图像坐标一致,并进行图像拼接。然后综合目标的位置信息和属性信息对多机图像进行目标关联。最后提出一种基于冲突度量的动态切换策略,自适应选择DST(dempster-shafer theory)或DSmT(desert-smarandache theory)融合关联目标信息。在多无人机协同目标识别数据集上进行实验,结果表明所提算法能在增大单次巡视范围的同时,提高无人机巡视系统的检测精度。     

导弹探测系统发现概率的建模

来袭弹道导弹的系统发现概率是度量导弹探测系统效能的关键指标。基于搜索论,根据远程地基预警雷达分别在无目标指示和红外预警卫星目标方位、运动状态指示下的场景,分别建立了其对来袭弹道导弹发现概率模型。根据集合论给出了系统发现概率的定义和表达式,并分析了有目标指示相对于无目标指示状态的增量。以“铺路爪”远程预警雷达为算例,给出了其有无目标指示等多种情况发现概率的结果。研究过程可应用到导弹探测系统的设计。     

Fault detection for nonlinear networked control systems based on fuzzy observer

Security and reliability must be focused on control systems firstly, and fault detection and diagnosis (FDD) is the main theory and technology. Now, there are many positive results in FDD for linear networked control systems (LNCSs), but nonlinear networked control systems (NNCSs) are less involved. Based on the T-S fuzzy-modeling theory, NNCSs are modeled and network random time-delays are changed into the unknown bounded uncertain part without changing its structure. Then a fuzzy state observer is designed and an observer-based fault detection approach for an NNCS is presented. The main results are given and the relative theories are proved in detail. Finally, some simulation results are given and demonstrate the proposed method is effective.     

基于等价空间的网络控制系统故障检测问题研究

研究了网络控制系统的故障检测问题。对于一类网络时延为不确定情况的网络控制系统,得到了系统的具有模型不确定性的离散时间模型。将等价空间故障检测方法中的等价向量扩展为等价矩阵的情况,进而可将残差产生器的设计问题归结为受线性矩阵不等式约束的多目标优化问题,并可应用线性矩阵不等式技术求解等价矩阵。同时还讨论了对残差评估问题。算例验证了提出方法的有效性。     

奇异时滞系统的鲁棒故障检测和分离观测器

讨论了一类不确定奇异时滞系统的鲁棒故障检测和分离观测器(RDO)设计问题,设计所有的观测器使得它们的冗余信号对非故障因素具有鲁棒性,并设计每个观测器使其冗余信号对一组特定的故障不敏感而对其余故障敏感。给出了RDO存在的充分条件及有效的设计算法,并讨论了故障集合不同划分的RDO设计之间的关系,最后给出一具体算例来说明该方法的有效性。     

基于模糊模型的无线网络控制系统故障检测

考虑控制器与被控对象之间采用无线传输,且被控对象为非线性模型的一类网络控制系统,对其进行故障检测。首先基于T-S模糊模型将对象线性化,利用模糊主导子系统规则,设计了模糊观测器,并得出了观测器误差方程。然后将误差方程等效为与无线传输跳数相关的离散切换系统,并证明了误差系统的稳定性。最后,通过仿真实例验证了所提方法的有效性。