基于噪声方差估计的红外弱小目标检测与跟踪方法

针对红外弱小多目标图像背景杂波干扰严重、弱 小目标检测率低和目标跟踪困难的问题,提出一种 基于噪声方差估计的红外弱小目标快速检测与目标跟踪算法。首先采用改进的形态学 滤波抑制背景噪声, 对处理后的多帧图像进行方差估计初步突出目标像素;然后对其进行信噪比(SNR)估计得到整个图像序列像素得 分,图像中像素SNR高的被标记为目标像素;再对标记过的图像进行分块分析 ,准确提取出连续图 像序列中的目标像素;将检测出的目标像素作为Hough变换的目标跟踪算法的输入,设置双 阈值实现目标 的有效跟踪。实验结果表明,在复杂背景下的红外弱小目标提取中,基于噪声方差估计的目 标检测拥有较 高的检测概率和较低的虚警概率,将其获得的目标像素作为Hough变换的输入,不仅可以有 效跟踪目标, 而且简化了算法的复杂度,实现目标的快速提取和跟踪,具有很高的应用价值。     

无源定位对机动目标跟踪研究

通过分析飞行目标的空间运动关系,建立符合飞行目标运动特性的PVT三维跟踪模型。因为飞行目标无源跟踪的观测模型的高非线性,并且状态向量维数较高,文章把粒子滤波应用到飞行目标跟踪,并与卡尔曼滤波跟踪结果比较。仿真试验验证了PVT模型和PF滤波算法跟踪飞行目标的优越性。     

基于雷达测量的多目标联合检测、跟踪与分类方法

利用雷达测量中的目标速度、加速度等属性信息, 基于跳转马尔科夫系统模型高斯混合概率假设密度滤波算法, 提出了一种多目标联合检测、跟踪与分类方法.该方法在进行雷达多目标测量信息处理的多模型混合高斯概率假设密度滤波过程中, 对各高斯项编号, 进行航迹提取, 在滤波处理的同时形成带有航迹编号的明确航迹, 并进行航迹管理; 同时, 根据目标运动模型, 联合利用目标加速度控制输入与速度估计进行多目标分类.仿真试验验证了该方法能够在检测、跟踪的同时, 对目标航迹进行有效类型识别.     

Automatic tracking, feature extraction and classification of C elegans phenotypes

This paper presents a method for automatic tracking of the head, tail, and entire body movement of the nematode Caenorhabditis elegans (C. elegans) using computer vision and digital image analysis techniques. The characteristics of the worm's movement, posture and texture information were extracted from a 5-min image sequence. A Random Forests classifier was then used to identify the worm type, and the features that best describe the data. A total of 1597 individual worm video sequences, representing wild type and 15 different mutant types, were analyzed. The average correct classification ratio, measured by out-of-bag (OOB) error rate, was 90.9%. The features that have most discrimination ability were also studied. The algorithm developed will be an essential part of a completely automated C. elegans tracking and identification system.     

一种机动海目标跟踪算法研究

在α-β跟踪滤波器的基础上增加了机动检测器,根据残差过程的变化特性,设计机动检测准则来提高对机动海目标的跟踪性能。仿真比较了原始α-β跟踪滤波器和加机动检测器的滤波算法性能。     

Detection and discrimination of radar targets

A new method for detection and discrimination of radar targets is described. The basis for this method is that the gross structure of a radar target can be identified from scattered fields of the target at harmonic radar frequencies located just in the low resonance region. This is in sharp contrast to many target signature schemes that operate at much higher frequencies and observe many of the details of the target in lieu of its gross features. Multiple frequency radar scattering data and the complex natural resonant frequencies of radar targets are integrated into a predictor-correlator processor. The method is illustrated using as target models both classical shapes and thin-wire configurations of simple geometry. Integral equation programs are utilized to calculate multiple frequency backscatter data for the wire geometries and to deduce the complex natural resonant frequencies of the wire structures. Discrete multiple frequency radar scattering data spanning a particular spectral range are shown to be desirable for optimum capability but discrimination and detection can be achieved using just two near-conventional radars, even if the radars are located at different sites and hence view the target from different aspects.     

Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy

Quantitative measurement of cell cycle progression in individual cells over time is important in understanding drug treatment effects on cancer cells. Recent advances in time-lapse fluorescence microscopy imaging have provided an important tool to study the cell cycle process under different conditions of perturbation. However, existing computational imaging methods are rather limited in analyzing and tracking such time-lapse datasets, and manual analysis is unreasonably time-consuming and subject to observer variances. This paper presents an automated system that integrates a series of advanced analysis methods to fill this gap. The cellular image analysis methods can be used to segment, classify, and track individual cells in a living cell population over a few days. Experimental results show that the proposed method is efficient and effective in cell tracking and phase identification.     

Discriminative metric preservation for tracking low-resolution targets

Tracking low-resolution (LR) targets is a practical yet quite challenging problem in real video analysis applications. Lack of discriminative details in the visual appearance of the LR target leads to the matching ambiguity, which confronts most existing tracking methods. Although artificially enhancing the video resolution by superresolution (SR) techniques before analyzing might be an option, the high demand of computational cost can hardly meet the requirements of the tracking scenario. This paper presents a novel solution to track LR targets without explicitly performing SR. This new approach is based on discriminative metric preservation that preserves the data affinity structure in the high-resolution (HR) feature space for effective and efficient matching of LR images. In addition, we substantialize this new approach in a solid case study of differential tracking under metric preservation and derive a closed-form solution to motion estimation for LR video. In addition, this paper extends the basic linear metric preservation method to a more powerful nonlinear kernel metric preservation method. Such a solution to LR target tracking is discriminative, robust, and efficient. Extensive experiments validate the entrustments and effectiveness of the proposed approach and demonstrate the improved performance of the proposed method in tracking LR targets.     

空中飞机目标噪声稳健分类方法

针对低信噪比条件下微多普勒调制易被噪声污染的问题,提出了一种基于复数域概率主成分分析(Complex Probabilistic Principal Component Analysis,CPPCA)模型的噪声稳健分类算法来实现低分辨雷达体制下三类飞机目标(喷气式飞机、螺旋桨飞机和直升机)的分类.算法依据三类飞机多普勒谱调制的差异,提出两维反映这种差异的微动特征.为了提高微动特征在低信噪比条件下的分类性能,利用CPPCA模型对雷达复回波信号建模并结合Akaike信息量准则(Akaike's Information Criterion,AIC)来自适应地确定回波中主成分的个数从而实现对数据的噪声抑制.基于实测数据的实验结果表明,该算法在较低信噪比条件下能够获得较好的噪声抑制和分类性能.     

Cloud tracking by scale space classification

The problem of cloud tracking within a sequence of geo-stationary satellite images has direct relevance to the analysis of cloud life cycles and to the detection of cloud motion vectors (CMVs). The proposed approach first identifies a homogeneous consistent cloud mass for tracking and then establishes motion correspondence within an image sequence. In contrast to the crosscorrelation based approach as adopted in automatic CMV detection analysis, a scale space classifier is designed to detect cloud mass in the source image taken at time t and the destination image at time t+δt. Boundaries of the extracted cloud segments are matched by computing a correspondence between high curvature points. This shape based method is capable of tracking in the cases of rotation, scaling, and shearing, while the correlation technique is limited to translational motion. The final tracking results provide motion magnitude and direction for each contour point, allowing reliable estimation of meteorological events and wind velocities aloft. With comparable computational expense, the scale space classification technique exceeds the performance of the traditional correlation-based approach in terms of reduced localization error and false matches     

雷达表盘图像移动目标的快速识别与跟踪

针对以往对机载雷达表盘的图像移动目标的识别与跟踪速度较慢,缺乏实时性的问题,提出一种快速识别和跟踪移动目标的方法。分析和总结了关于移动物体连续两帧图像的特点,并根据它们在图像对比中的空间位移上的相对变化关系,运用OTSU算法、差影法、直方图统计特征分析法,对移动目标进行识别与跟踪。该方法处理一帧图像的时间不超过40ms,具有识别速度快、跟踪准确、抗干扰能力强等特点。     

Distributed target classification and tracking in sensor networks

The highly distributed infrastructure provided by sensor networks supports fundamentally new ways of designing surveillance systems. In this paper, we discuss sensor networks for target classification and tracking. Our formulation is anchored on location-aware data routing to conserve system resources, such as energy and bandwidth. Distributed classification algorithms exploit signals from multiple nodes in several modalities and rely on prior statistical information about target classes. Associating data to tracks becomes simpler in a distributed environment, at the cost of global consistency. It may be possible to filter clutter from the system by embedding higher level reasoning in the distributed system. Results and insights from a recent field test at 29 Palms Marine Training Center are provided to highlight challenges in sensor networks.     

Modified Moose estimator for tracking highly manoeuvring targets

The Moose's adaptive state estimator has proved successful in tracking a manoeuvring submarine. However, its application to track highly manoeuvring aircraft encounters problems in complexity and tracking accuracy. The authors present a new algorithm that extends the Moose estimator to solve its problems. The new algorithm is verified through Monte-Carlo simulation and shows a better performance than the original Moose algorithm     

复杂地面场景下的红外运动目标跟踪

复杂地面场景下的红外目标易受背景影响并经常出现遮挡情况,难以简单地依靠亮度或梯度信息检测并跟踪目标。根据复杂背景下红外运动目标与背景的速度场差异,提出了利用光流对目标进行跟踪的算法。首先对图像进行配准,保证在随动跟踪时背景的相对静止;然后在目标的跟踪波门内计算改进的Horn-Schunck 光流;最后根据目标的光流特征,优化粒子滤波算法中粒子的转移概率,实现对目标的稳健跟踪。实验结果表明,该跟踪算法能对复杂地面场景下的红外运动目标持续跟踪,并不受目标被短时遮挡的影响。     

超低空目标定高跟踪处理算法的研究

对超低空目标的稳定跟踪一直是舰载三坐标雷达的一个难点。本文提出的利用定高跟踪的算法实现对超低空目标的跟踪是一个全新的方法,在工程实践中具有一定的实用价值。本文主要介绍了定高跟踪的基本原理、实现方法和仿真结果,并对今后研究重点作了展望。     

一种新的多装甲目标在线跟踪方法

实现多装甲目标跟踪在协同跟踪和打击中发挥着至关重要的作用,而实现多装甲目标跟踪需要解决目标之间遮挡、轨迹交叉以及目标尺度不断变化等问题。因此,提出了一种基于视觉注意力的Gabor在线多装甲目标跟踪方法,实现对地面战场中多装甲目标的跟踪。通过模拟视网膜结构,构造了一种能够增强检测的视觉注意力Gabor分支,并引入时间信息,采用在线学习的目标特性卷积神经网络解决目标遮挡的问题,更重要的是,通过实际拍摄和互联网下载等手段构建了多装甲目标跟踪数据集,并且通过对目前成熟的多目标跟踪方法和本文提出的方法进行对比实验。实验证明了本文提出的方法不仅具有优异的跟踪性能,而且能够满足实际的应用需求。     

A NEW MODELING AND FILTERING APPROACH FOR TRACKING MANEUVERING TARGETS

A new modeling and filtering approach for tracking maneuvering targets is presented in thispaper.The approach,which makes optimal estimate for the model With the random variable possible,depends on random step modeling of target maneuvers.In the new model,the unknown targetacceleration is treated as a random variable and then estimated directly.A detector is designed tofind out the target maneuvers and the estimation algorithm will be restarted when the maneuvers oc-cur.Combination of three-dimention Kalman filter with a detector forms a tracker for maneuveringtargets.The new tracking scheme is easy to implement and its capability is illustrated in two trackingexamples in which the new approach is compared with Mooses'on the performance.     

机载单站对机动目标无源定位与跟踪

机载无源探测定位技术是机载电子对抗中的一个重要的研究领域。针对空间机动目标,建立了机载单站无源定位的三维模型。基于传统测量相位差变化率的方法,增加了多普勒及其变化率信息,探讨了其定位原理,重新推导了定位公式;给出了其中的预处理过程和系统状态方程,并引入了可调白噪声UKF滤波算法以提高对机动目标的跟踪定位精度和收敛速度。通过计算机仿真,验证了该方法的正确性及有效性。     

一种改进的Mean Shift运动目标跟踪算法

首先研究了Mean Shift算法(均值漂移算法),针对Mean Shift算法在跟踪视频运动物体应用中的不足,提出了将卡尔曼滤波预测的窗口和三帧差法提取的窗口通过加权的方式,产生一个新的检测窗口作为均值漂移算法的检索窗口,同时对核函数带宽和目标模型进行更新。实验表明经过优化的算法,对运动物体跟踪有了明显的改进,有了更好的鲁棒性。     

实时CCD成像跟踪处理系统研究

提出了基于高速数字信号处理器TMS320C6415的图像识别跟踪系统，并给出了成像跟踪系统的原理图和结构图。重点论述了采集系统和图像处理系统。在软件上采用矩不变阈值分割和基于梯度的自适应阂值分割提取目标，采用投影特征的质心跟踪算法及基于线性微分拟合的记忆外推跟踪技术跟踪目标。实验结果表明：该系统跟踪稳定可靠，完全满足实时性要求。