高速运动目标视频序列分层技术研究

视频序列分层技术是视频内容检索系统的关键部分 ,讨论了目标高速运动下的视频序列分层技术。采用高阶统计量、图像能量理论及块处理技术 ,快速拼接、重建视频序列中各镜头的背景 ,利用重建背景进行视频分层。实际视频序列的测试结果验证了该算法的有效性 ,层次分割的准确性 ,适用于实际高速运动视频序列的层次分割。

弹目相对运动特性加载技术框架初探

目前在靶场如何开展导引头抗干扰内外场联合试验已经成为急需解决的课题,其中弹目相对运动加载技术是制约该课题发展的瓶颈。针对这个问题,研究了弹目相对运动特性加载技术,提出了弹目相对运动加载方案。目标模拟器产生的模拟目标与导引头在俯仰和方位上角度的相对运动,采用X-Y轴扫描器的方式来实现,模拟目标与导引头在距离上的相对运动采用距离拖引,外加多普勒频移的方式来实现。并介绍了一种目标模拟器的实现方法,上述探讨的方法对靶场进行弹目相对运动特性加载工作具有一定的指导意义。

分数阶Fourier变换在SAR动目标检测中的应用

基于分数阶Fourier变换与时频分布的关系,利用合成孔径雷达运动目标回波信号本质上为线性调频信号的特点,在分数阶Fourier域对运动目标进行检测和多普勒参数估计,并通过理论分析和计算机仿真证明了该方法的有效性.

防空导弹尾追拦截目标的遭遇点预测方法

为充分发挥导弹武器系统的全向拦截能力,防空导弹需要面对拦截发射时即处于尾追逃逸态势目标的问题。结合防空导弹任务特点,设计了一种兼顾迎击、尾追拦截态势的遭遇点预测方法。根据导弹与目标在发弹时刻和拦截过程中的相对运动关系,建立常规迎击态势下的遭遇点预测模型。针对尾追拦截的特殊情况,分析预测模型对弹目相对运动关系描述的适用性,并进一步研究目标初始参数对预测模型收敛性的影响。在此基础上优化运动几何关系描述模型,结合收敛条件设计迭代算法,形成尾追拦截态势下遭遇点的预测方法。该方法修正了目标穿越发射点上空引起的模型适用性问题。仿真结果表明:迭代算法对迎击、尾追目标态势均快速收敛,预测精度满足工程应用要求,为导弹全向拦截时的遭遇点预测提供了有效的解决途径。

SAR Moving Target Detection and Imaging Based on WVH Transform

Aim To propose a generalized and closed representation of the Wigner Ville Hough transform(WVHT), for the moving target detection and imaging in the design of synthetic aperture radar(SAR). Methods Based on the line integral, the WVH transform was derived by combining the Wigner Ville distribution (WVD) and the Hough transform (HT) together. The new transform was then verified with computer by the simulated SAR echoes. Results and Conclusion The correctness and the validity of the WVH transform were proved by the computer simulation. Compared with the conventional WVD HT method, the new approach based on the WVHT can simplify the processing procedure, it can translate the chirp echoes of multi targets of SAR from the time domain into the parameter space directly, while suppressing the cross terms of WVD and estimating the motion coefficients for the final imaging. It is obvious that the WVH transform can be also used in other cases for the chirp signal detection.

Fast global motion estimation and moving object extraction algorithm in image sequences

A novel and effective approach to global motion estimation and moving object extraction is proposed. First, the translational motion model is used because of the fact that complex motion can be decomposed as a sum of translational components. Then in this application, the edge gray horizontal and vertical projections are used as the block matching feature for the motion vectors estimation. The proposed algorithm reduces the motion estimation computations by calculating the onedimensional vectors rather than the two-dimensional ones. Once the global motion is robustly estimated, relatively stationary background can be almost completely eliminated through the inter-frame difference method. To achieve an accurate object extraction result, the higher-order statistics （HOS） algorithm is used to discriminate backgrounds and moving objects. Experimental results validate that the proposed method is an effective way for global motion estimation and object extraction.

MOVING OBJECT TRACKING IN DYNAMIC IMAGE SEQUENCE BASED ON ESTIMATION OF MOTION VECTORS OF FEATURE POINTS

An improved estimation of motion vectors of feature points is proposed for tracking moving objects of dynamic image sequence. Feature points are firstly extracted by the improved minimum intensity change （MIC） algorithm. The matching points of these feature points are then determined by adaptive rood pattern searching. Based on the random sample consensus （RANSAC） method, the background motion is finally compensated by the parameters of an affine transform of the background motion. With reasonable morphological filtering, the moving objects are completely extracted from the background, and then tracked accurately. Experimental results show that the improved method is successful on the motion background compensation and offers great promise in tracking moving objects of the dynamic image sequence.

Adaptive moving target detection algorithm based on Gaussian mixture model

In order to enhance the reliability of the moving target detection, an adaptive moving target detection algorithm based on the Gaussian mixture model is proposed. This algorithm employs Gaussian mixture distributions in modeling the background of each pixel. As a result, the number of Gaussian distributions is not fixed but adaptively changes with the change of the pixel value frequency. The pixels of the difference image are divided into two parts according to their values. Then the two parts are separately segmented by the adaptive threshold, and finally the foreground image is obtained. The shadow elimination method based on morphological reconstruction is introduced to improve the performance of foreground image＇s segmentation. Experimental results show that the proposed algorithm can quickly and accurately build the background model and it is more robust in different real scenes.

纵论“肝者罢极之本”之本意

就《内经》中"肝者,罢极之本"的本意进行探讨,提出己见与历代注家进行辨析。认为"罢极"在此处的本意是:表示"闭目"、"开目"两种相反的目睛运动之生理功能。"罢"——罢上目睛之"闭目"、"极"——张开目睛之"开目"。肝是人们"开目"、"闭目"运动的枢纽,肝之气血通过经络输送到上窍目睛,并且控制和协调目睛的运动,从而产生"闭目"、"开目"功能,所以肝是人们"闭目"、"开目"运动的根本。

Detection technique of moving target based on passive millimeter wave

To solve the problem of insufficient ability when detecting the high-speed moving target with passive millimeter wave technology, a direct-detection passive millimeter wave detecting system using the monolithic microwave integrated cir- cuit （MMIC） millimeter wave radiometer is built, and the measured data are obtained by experiment under different condi- tions. Based on feature analysis of testing signals, it points out that the peak of the first pulse and interval of two peak pulses are valid features which can reflect the motion characteristic of target. A method to calculate the moving speed of target is put forward. The calculating results indicate that the proposed method has enough accuracy and is feasible to determine the parameters of the moving target using for passive millimeter wave system.

非线性H_∞导引律设计与分析

针对导弹拦截目标制导过程中的测量噪声和干扰以及未知目标机动,本文提出了具有严格解析形式的三维非线性H∞导引律的设计方法。通过构建基于改进极坐标系下的三维弹目相对运动方程和存在多种测量噪声和干扰下的弹目相对运动方程,归纳推导出简洁的导弹拦截目标的系统方程;利用非线性H∞控制理论构造相应的HJI偏微分方程不等式,并且提出了HJI偏微分方程不等式的一种解法,得到了一组解析解,同时得到了拦截系统的一个正定储能函数,进而构造了非线性H∞导引律;通过分析非线性H∞导引律的设计方法和几项关键的制导拦截影响因素,定性指出了如何调整设计参数能够有效对抗各种不利因素,提高制导性能;最后,仿真研究验证了非线性H∞导引律的有效性和定性分析的有效性。

一种瞄准式战斗部瞄准控制方案

针对传统导弹的战斗部无法实现对目标在俯仰和方位上双定向的问题,为具有瞄准式战斗部的导弹建立了破片与目标相对运动模型。根据该模型及战斗部破片在不同瞄准角下飞散距离不同的特点,将战斗部中心破片最佳飞散距离作为瞄准攻击的依据,实现了瞄准式战斗部破片飞散方向的双向控制,推导出了引信起爆延迟时间的解析解。通过仿真证明了在现有的目标与导弹相对速度及脱靶量测量误差条件下,基于最佳飞散距离的瞄准方案可以应用于瞄准式战斗部的方向控制。

Moving target detection based on improved ghost suppression and adaptive visual background extraction

Visual background extraction algorithm(ViBe)uses the first frame image to initialize the background model,which can easily introduce the“ghost”.Because ViBe uses the fixed segmentation threshold to achieve the foreground and background segmentation,the detection results in many false detections for the highly dynamic background.To solve these problems,an improved ghost suppression and adaptive Visual Background Extraction algorithm is proposed in this paper.Firstly,with the pixel’s temporal and spatial information,the historical pixels of a certain combination are used to initialize the background model in the odd frames of the video sequence.Secondly,the background sample set combined with the neighborhood pixels are used to determine a complex degree of the background,to acquire the adaptive segmentation threshold.Thirdly,the update rate is adjusted based on the complexity of the background.Finally,the detected result goes through a post-processing to achieve better detection results.The experimental results show that the improved algorithm will not only quickly suppress the“ghost”,but also have a better detection in a complex dynamic background.

SADDLE-POINT BASED SEPARATION OF TOUCHED OBJECTS IN 2-D IMAGE

In many image analysis and processing problems, discriminating the size and shape of each individual object in an aggregate pile projected in an image is an important practice. It is relatively easy to distinguish these features among the objects already separated from each other. The problems will be undoubtedly more complex and of greater challenge if the objects are touched or/and overlapped. This letter presents an algorithm that can be used to separate the touches and overlaps existing in the objects within a 2-D image. The approach is first to convert the gray-scale image to its corresponding binary one and then to the 3-D topographic one using the erosion operations. A template (or mask) is engineered to search the topographic surface for the saddle point, from which the segmenting orientation is determined followed by the desired separating operation. The algorithm is tested on a real image and the running result is adequately satisfying and encouraging.

Background Subtraction and Frame Difference Based Moving Object Detection for Real-Time Surveillance

A new real-time algorithm is proposed in this paperfor detecting moving object in color image sequencestaken from stationary cameras.This algorithm combines a temporal difference with an adaptive background subtraction where the combination is novel.Ⅷ1en changes OCCUr.the background is automatically adapted to suit the new conditions.Forthe background model,a new model is proposed with each frame decomposed into regions and the model is based not only upon single pixel but also on the characteristic of a region.The hybrid presentationincludes a model for single pixel information and a model for the pixel’s neighboring area information.This new model of background can both improve the accuracy of segmentation due to that spatialinformation is taken into account and salientl5r speed up the processing procedure because porlion of neighboring pixel call be selected into modeling.The algorithm was successfully used in a video surveillance systern and the experiment result showsit call obtain a clearer foreground than the singleframe difference or background subtraction method.

An improved Vibe algorithm for illumination mutations

The visual background extractor(Vibe)algorithm can lead to a large area of false detection in the extracted foreground target when the illumination is mutated.An improved Vibe method based on the YCbCr color space and improved three-frame difference is proposed in this paper.The algorithm detects the illumination mutation frames accurately based on the difference between the luminance components of two frames adjacent to a video frame.If there exists a foreground moving target in the previous frame of the mutated frame,three-frame difference method is utilized;otherwise,Vibe method using current frame is used to initialize background.Improved three-frame differential method based on the difference in brightness between two frames of the video changes the size of the threshold adaptively to reduce the interference of noise on the foreground extraction.Experiment results show that the improved Vibe algorithm can not only suppress the“ghost”phenomenon effectively but also improve the accuracy and completeness of target detection,as well as reduce error rate of detection when the illumination is mutated.

Numerical Modeling of Floating Oil Boom Motions in Wave-Current Coupling Conditions

Containment booms are commonly used in collecting and containing spilled oil on the sea surface and in protecting specific sea areas against oil slick spreading.In the present study,a numerical model is proposed based on the N-S equations in a mesh frame.The proposed model tracks the outline of the floating boom in motion by using the fractional area/volume obstacle representation technique.The boom motion is then simulated by the technique of general moving object.The simulated results of the rigid oil boom motions are validated against the experimental results.Then,the failure mechanism of the boom is investigated through numerical experiments.Based on the numerical results,the effects of boom parameters and dynamic factors on the oil containment performance are also assessed.

An improved method for multiple targets tracking

The difficulty of multiple targets tracking is how to quickly fulfill the target matching from one flame image to another and fix the position of the target. In order to accurately choose target feature information for reliable matching, simplify operations under the reliable precondition, and realize precise moving objects tracking, an approach based on Kalman prediction and feature matching was proposed. The position of the target in next frame image was predicted by Kalman, and then the moving objects of two adjacent frames were matched by the centroid and area methods. When occlusion occurs, the best matching result was found to realize tracking by matching matrix algorithm. The simulation results show that the proposed method can achieve multiple targets tracking accurately and in real-time under complicated motion movements.

Linear Track Estimation Using Double Pulse Sources for Near-Field Underwater Moving Target

The double pulse sources （DPS） method is presented for linear track estimation in this work. In the field of noise identification of underwater moving target, the Doppler will distort the frequency and amplitude of the radiated noise. To eliminate this, the track estimation is necessary. In the DPS method, we first estimate bearings of two sinusoidal pulse sources installed in the moving target through baseline positioning method. Meanwhile, the emitted and recorded time of each pulse are also acquired. Then the linear track parameters will be achieved based on the geometry pattern with the help of double sources spacing. The simulated results confirm that the DPS improves the performance of the previous double source spacing method. The simulated experiments were carried out using a moving battery car to further evaluate its performance. When the target is 40-60m away, the experiment results show that biases of track azimuth and abeam distance of DPS are under 0.6° and 3.4m, respectively. And the average deviation of estimated velocity is around 0.25m/s.

Moving object detection method based on complementary multi resolution background models

A novel moving object detection method was proposed in order to adapt the difficulties caused by intermittent object motion,thermal and dynamic background sequences.Two groups of complementary Gaussian mixture models were used.The ghost and real static object could be classified by comparing the similarity of the edge images further.In each group,the multi resolution Gaussian mixture models were used and dual thresholds were applied in every resolution in order to get a complete object mask without much noise.The computational color model was also used to depress illustration variations and light shadows.The proposed method was verified by the public test sequences provided by the IEEE Change Detection Workshop and compared with three state-of-the-art methods.Experimental results demonstrate that the proposed method is better than others for all of the evaluation parameters in intermittent object motion sequences.Four and two in the seven evaluation parameters are better than the others in thermal and dynamic background sequences,respectively.The proposed method shows a relatively good performance,especially for the intermittent object motion sequences.