复杂背景下运动目标检测方法

利用相邻两帧图像的运动信息更新特征块提取阈值,实时地提取出当前帧图像中最能体现背景运动特征的特征块。采用块匹配的运动估计方法获取了各块的运动矢量,再依据所有特征块运动矢量的统计特性提取出背景运动矢量来配准差分,实现了目标的检测。实验结果表明,此方法能有效地检测复杂背景下的运动目标。     

复杂背景下红外运动点目标检测算法研究

讨论了复杂背景下低信噪比运动点目标的检测和跟踪问题，提出了基于卡尔曼滤波理论的背景预测，数学形态学膨胀累加，图像流航迹关联和二级并行假设检验的点目标检测方法。实验结果表明该算法能够较大程度提高红外图像的信噪比，有效地检测和跟踪点目标，并且能够解决目标丢失以及跟踪过程中出现目标的问题。     

一种天地复杂背景下的红外弱小目标检测方法

针对天地复杂背景下红外弱小目标的检测,提出了一种融合top-hat变换、边缘检测的新方法.首先将改进的top-hat变换应用于红外弱小目标图像,将图像中与目标特性相似的像素进行增强,并去除云层背景对目标的干扰,得到若干疑似目标的增强结果,并结合阈值分割方法对增强结果进行筛选,剔除大部分不符合弱小红外目标特性的背景干扰点.然后采用基于Canny算子的边缘提取方法对原始图像中的天地线进行检测,并以天地分割线作为先验知识,去除形态学滤波后所增强的地物背景中的疑似目标,得到最终的真实目标的检测结果.通过实验得出,本文所提出的方法对于天地背景下的弱小红外目标具有良好的检测效果.     

自适应复杂背景干扰的运动目标检测算法

基于视频图像的运动目标检测,是根据目标的像素特征来判别出相对于背景运动的目标,当图像背景动态变化时,将难以区分背景和运动目标的像素特征,易造成检测错误.复杂背景下的运动目标检测是一大难点,目前主流的运动目标检测算法在背景灰暗、水面波动、气流颤动等复杂背景干扰下,难以准确地检测出运动目标.针对上述问题,提出一种自适应复杂背景干扰的运动目标检测算法,采用新的前景判断和背景模型更新方法,同时设计了一种创新型自适应阈值更新方法,当视频背景变化时,自动更新阈值.该算法增强了对复杂背景、镜头抖动的抗干扰能力,通过各种视频测试,背景点检测正确率达到0.9958,前景点检测正确率达到0.8012,极大提高了前景检测率,而且该算法满足高实时性要求,对复杂背景下的运动目标检测有显著效果.     

海空复杂背景下红外弱点目标的检测算法

为第空复杂背景下红外弱点目标的检测,提出了基于小波变换模极大的检测算法,该算法通过计算小波变换模极大值求出图像中的所有奇异点,去除由复杂背景形成的模极大值链,消除云层、海浪及水天线等复杂背景,提高了单帧点目标检测能力,结果表明,余步坷检测信杂比为２的点目标。     

复杂背景下红外点目标的检测

在分析红外点目标、噪声及杂波模型的基础上,提出了基于二次滤波的复杂背景下红外点目标的检测算法,该算法由二阶滤波器组成。根据点目标的模型特点,通过选择合适的高通滤波器,可有效地增强信噪比、抑制噪声。实验结果表明该算法效果良好。     

一种复杂背景下运动小目标的检测方法

本文将小目标的帧间信息和光流法紧密联系起来,把小目标的检测分为小目标的预处理、帧间差分和使用金字塔迭代Lucas-Kanade的光流法确定目标三个步骤进行．实验结果表明,该方法能够有效地检测运动小目标．     

野外复杂背景下红外图像的目标检测

野外复杂背景下红外图像序列目标检测是红外野外监视中的重点问题。大量的背景物增加了目标检测的难度。文中针对野外复杂背景下红外图像序列的特点,提出了一种实用的运动目标检测算法。该算法包括两个处理步骤：首先,在场景配准后利用帧间差图像提取目标的运动信息,并据此进行目标的粗检测;其次,合目标运动在时间和空间上的相关性进行精检测。粗检测的低漏判度和精检测的低误差率保证了算法的可靠性。在检测的同时算法确定了目     

复杂背景下的运动目标检测方法

提出了一种复杂背景下的运动目标检测新方法,利用仿射变换进行全局运动估计,并利用双线性内插进行背景补偿,然后采用3帧对称差分相乘的方法增强运动目标的像素点,拉大目标与背景残留噪声的差异,最后利用区域生长法进行目标分割。用复杂的城市交通序列图像进行测试,并与经典的动目标检测算法进行对比,说明了算法的有效性。     

复杂背景下的动目标检测技术

目标检测是光电成像武器系统中图像处理的重要内容。本文研究了光流估计的基本算法。从运动分割的角度提出了使用光流分割方案来解决复杂背景下的动目标检测问题。文中分析了标准光流估计的缺陷及其原因．并使用坚韧性估计技术提高了光流估计算法的实用性。最后给出了动目标检测算法的仿真结果。其结果表明利用图像的运动特征来实现目标检测是一种很有效的方法。     

移动背景下的运动目标跟踪

运动目标跟踪在工业过程控制、医学研究、成像制导等领域具有重要的实用价值.目前的研究多基于背景静止的情况,对背景发生移动的情况研究较少.提出了一套完整的移动背景下的目标跟踪算法,首先使用基于互信息的方法配准序列图像的背景,然后使用差分的方法进行运动区域检测,并将其与图像分割技术相结合,得到目标跟踪模板.目标的跟踪基于Ka...     

港口背景下红外运动多目标跟踪

介绍了一种采用小波变换抑制港口动态背景的方法.在此基础上,运用基于Chan-Vese模型的水平集方法实现图像区域分割和粒子区域的特征提取,提取的特征包括结构和灰度特征,结合这些粒子区域特征和先验知识,在对标准粒子滤波进行改进的基础之上,实现多目标跟踪中的数据关联和目标聚类.同时,在多目标跟踪中会出现"新目标出现、目标消失、多目标合并与分裂、目标受遮挡"等现象,根据图像能量和轮廓形状间距离的变化来判断和解决上述现象带来的误跟踪问题.通过对实际港口背景条件下的红外序列图像进行多目标跟踪实验,验证了所提方法的可行性和有效性.     

Efficient multiple moving object detection and tracking using combined background subtraction and clustering

Object detection and tracking is a fundamental, challenging task in computer vision because of the difficulties in tracking. Continuous deformation of objects during movement and background clutter leads to poor tracking. In this paper, a method of multiple moving object detection and tracking by combining background subtraction and K-means clustering is proposed. The proposed method can handle objects occlusion, shadows and camera jitter. Background subtraction filters irrelevant information, and K-means clustering is employed to select the moving object from the remaining information, and it is capable of handling merging and splitting of moving objects using spatial information. Experimental results show that the proposed method is robust when compared to other techniques.     

基于OpenCV的交通视频运动目标检测与跟踪

智能交通系统（ITS）是目前世界交通运输领域正在研究和广泛关注的课题。OpenCV是一种用于数字图像处理和计算机视觉的函数库,由Intel公司开发。本文在目标检测方面,对采集到的交通视频进行灰度化、中值滤波、背景建模、二值化,背景差分等处理,可以较准确地检测出运动目标。在目标跟踪方面,提出了CamShift算法和Kalman滤波器相结合的方法,实现视频车辆的精确跟踪。最后,利用OpenCV的运动物体跟踪的数据结构、函数库,建立了一个视频车辆分析系统。用于道路上车辆的检测与跟踪,并具有良好的鲁棒性。     

Automatic moving object and background separation

In this paper, we propose a segmentation method of reduced computational complexity aimed at separating the moving objects from the background in a generic video sequence. This task may be accomplished at the coder site to support the functionalities foreseen by new multimedia scenarios, and in particular the content-based functionalities focused by the MPEG-4 activity, allowing the user to access and decode single objects of a video sequence. The proposed algorithm discriminates between background and foreground by means of a higher-order statistics (HOS) significance test performed on a group of inter-frame differences, followed by a motion detection phase, producing a binary segmentation map. The HOS threshold is adaptively changed, based on the estimated background activity and on the potential presence of slowly moving objects. The map is refined by a final regularization stage implemented by means of a cascade of morphological filters. The algorithm performance were tested through the wide experimental activity carried out during the ISO MPEG-4 N2 Core Experiment on Automatic Segmentation Techniques, in which the authors are currently involved. Typical results obtained on MPEG4 sequences are here shown, in order to illustrate the segmentation algorithm performance.     

运动目标的自动分割与跟踪

该文提出了一种对视频序列中的运动目标进行自动分割的算法。该算法分析图像在L U V空间中的局部变化,同时使用运动信息来把目标从背景中分离出来。首先根据图像的局部变化,使用基于图论的方法把图像分割成不同的区域。然后,通过度量合成的全局运动与估计的局部运动之间的偏差来检测出运动的区域,运动的区域通过基于区域的仿射运动模型来跟踪到下一帧。为了提高提取的目标的时空连续性,使用Hausdorff跟踪器对目标的二值模型进行跟踪。对一些典型的MPEG-4测试序列所进行的评估显示了该算法的优良性能。     

静态背景下运动目标边缘提取

针对运动目标在单帧图像中所占的比例较小,传统的边缘提取方法对整幅图像计算时产生大量冗余,对噪声敏感,提取出的运动目标轮廓不明显,提出一种基于帧差法和图像分块相结合的运动目标边缘检测方法。该方法首先对序列图像进行差分,按照特定的阈值对图像进行分块,完成对运动目标的细分割,分离出运动区域和非运动区域,然后对分割出来的运动区域进行边缘检测,将边缘检测结果和差分结果进行与运算,从而提取出运动目标轮廓。实验结果证明,分块边缘检测方法能较为准确地提取出运动目标且能提取出清晰的运动目标边缘轮廓,能满足实时性。     

基于改进高斯混合模型的体育视频运动目标检测与跟踪

针对当前体育视频运动目标检测存在的弊端,提出改进高斯混合模型的体育视频运动目标检测与跟踪方法。通过分析高斯混合模型的弊端,保留原有的"背景重建-模型更新-背景更新-目标检测"处理进程,将彩色图像转换为灰度图像,在像素相似度差别小的背景区域进行动态扩张,加入基于灰度直方图的目标跟踪进程,提高高斯混合模型对体育视频运动目标的处理效率与精度。实验结果表明,所提方法对体育视频的干扰处理能力强,检测范围大,检测与跟踪效果好。     

Probabilistic Kalman filter for moving object tracking

Kalman filter has been successfully applied to tracking moving objects in real-time situations. However, the filter cannot take into account the existing prior knowledge to improve its predictions. In the moving object tracking, the trajectories of multiple targets in the same environment could be available, which can be viewed as the prior knowledge for the tracking procedure. This paper presents the probabilistic Kalman filter (PKF) that is able to take into account the stored trajectories to improve tracking estimation. The PKF has an extra stage after two steps of the Kalman filter to refine the estimated position of the targets. The refinement is obtained by applying the Viterbi algorithm to a probabilistic graph, that is constructed based on the observed trajectories. The graph is built in the offline situation and could be adapted in the online tracking. The proposed tracker has higher accuracy compared to the standard Kalman filter and could handle widespread problems such as occlusion. Another significant achievement of the proposed tracker is to track an object with anomalous behaviors by drawing an inference based on the constructed probabilistic graph. The PKF was applied to several manually-built videos and several other video-bases containing severe occlusions, which demonstrates a significant performance in comparison with other state-of-the-art trackers.     

Efficient in-network moving object tracking in wireless sensor networks

The rapid progress of wireless communication and embedded microsensing MEMS technologies has made wireless sensor networks possible. In light of storage in sensors, a sensor network can be considered as a distributed database, in which one can conduct in-network data processing. An important issue of wireless sensor networks is object tracking, which typically involves two basic operations: update and query. This issue has been intensively studied in other areas, such as cellular networks. However, the in-network processing characteristic of sensor networks has posed new challenges to this issue. In this paper, we develop several tree structures for in-network object tracking which take the physical topology of the sensor network into consideration. The optimization process has two stages. The first stage tries to reduce the location update cost based on a deviation-avoidance principle and a highest-weight-first principle. The second stage further adjusts the tree obtained in the first stage to reduce the query cost. The way we model this problem allows us to analytically formulate the cost of object tracking given the update and query rates of objects. Extensive simulations are conducted, which show a significant improvement over existing solutions.