Moving Target Detection and Tracking for Smartphone Automatic Focusing

In this paper,a non-contact auto-focusing method is proposed for the essential function of auto-focusing in mobile devices.Firstly,we introduce an effective target detection method combining the 3-frame difference algorithm and Gauss mixture model,which is robust for complex and changing background.Secondly,a stable tracking method is proposed using the local binary patter feature and camshift tracker.Auto-focusing is achieved by using the coordinate obtained during the detection and tracking procedure.Experiments show that the proposed method can deal with complex and changing background.When there exist multiple moving objects,the proposed method also has good detection and tracking performance.The proposed method implements high efficiency,which means it can be easily used in real mobile device systems.

Dynamic Target Detection and Tracking Based on Quantum Illumination LIDAR

In the detection process of classic radars such as radar/lidar,the detection performance will be weakened due to the presence of background noise and loss.The quantum illumination protocol can use the spatial correlation between photon pairs to improve image quality and enhance radar detection performance,even in the presence of loss and noise.Based on this quantum illumination LIDAR,a theoretic scheme is developed for the detection and tracking of moving targets,and the trajectory of the object is analyzed.Illuminated by the quantum light source as Spontaneous Parametric Down-Conversion(SPDC),an opaque target can be identified from the background in the presence of strong noise.The static objects obtained by classical and quantum illumination are compared,respectively,and the advantages of quantum illumination are verified.The moving objects are taken at appropriate intervals to obtain the images of the moving objects,then the images are visualized as dynamic images,and the three-frame difference method is used to obtain the target contour.Finally,the image is performed by a series of processing on to obtain the trajectory of the target object.Several different motion situations are analyzed separately,and compared with the set object motion trajectory,which proves the effectiveness of the scheme.This scheme has potential practical application value.

Small tracking error correction for moving targets of intelligent electro-optical detection systems

Small tracking error correction for electro-optical systems is essential to improve the tracking precision of future mechanical and defense technology.Aerial threats,such as“low,slow,and small(LSS)”moving targets,pose increasing challenges to society.The core goal of this work is to address the issues,such as small tracking error correction and aiming control,of electro-optical detection systems by using mechatronics drive modeling,composite velocity–image stability control,and improved interpolation filter design.A tracking controller delay prediction method for moving targets is proposed based on the Euler transformation model of a two-axis,two-gimbal cantilever beam coaxial configuration.Small tracking error formation is analyzed in detail to reveal the scientific mechanism of composite control between the tracking controller’s feedback and the motor’s velocity–stability loop.An improved segmental interpolation filtering algorithm is established by combining line of sight(LOS)position correction and multivariable typical tracking fault diagnosis.Then,a platform with 2 degrees of freedom is used to test the system.An LSS moving target shooting object with a tracking distance of S=100 m,target board area of A=1 m^(2),and target linear velocity of v=5 m/s is simulated.Results show that the optimal method’s distribution probability of the tracking error in a circle with a radius of 1 mrad is 66.7%,and that of the traditional method is 41.6%.Compared with the LOS shooting accuracy of the traditional method,the LOS shooting accuracy of the optimized method is improved by 37.6%.

METHOD OF MULTI-TARGET TRACKING IN WIDE AREA SURVEILLANCE AIRBORNE RADAR SYSTEM BASING ON CLUSTERING ANALYSIS

This paper proposed a robust method based on the definition of Mahalanobis distance to track ground moving target. The feature and the geometry of airborne ground moving target tracking systems are studied at first. Based on this feature, the assignment relation of time-nearby target is calculated via Mahalanobis distance, and then the corresponding transformation formula is deduced. The simulation results show the correctness and effectiveness of the proposed method.

Target detection method for moving cows based on background subtraction

Target detection is the fundamental work for perceiving the behavior of cows using video analysis automatically.The videos captured in farming scenes often suffer from a complex background,which leads to difficulty in detecting the target and inconvenience in the subsequent images analysis.In this study,a method was proposed to detect the moving target accurately for cows based on background subtraction.Firstly,the bounding rectangle of cows was calculated using the frames difference method to extract the local background in frames,which were averaged and spliced into one image as the entire background image.Secondly,the size and location of a cow’s body were determined by the bounding rectangle of cows,and the body area was tracked through the video by the binary images.Thirdly,the summation coefficients on RGB channels were adjusted to improve the contrast between the target and background images.Finally,taking the body area in every frame as reference area,the performance of target detection was evaluated by the reference area to determine the optimal summation coefficients on RGB channels,and then background subtraction was processed again to finish the detection.A total of 129 videos were used to test the detection algorithm,and the accuracy of the algorithm was 88.34%,which was 24.85%higher than the classical background subtraction method.The study shows that the algorithm proposed in this study is feasible to detect the target accurately and timely when cows are walking straight in the farming environment under natural light,and this method can improve the detection performance and is an extension to the classical background subtraction method.

基于精确背景运动补偿的机器人运动目标检测与跟踪

为实现动态场景下机器人快速准确地检测与跟踪运动目标,提出一种基于精确背景补偿的运动目标检测方法,并利用Kalman滤波扩展的KCF算法进行目标跟踪。针对传统ORB算法存在特征点分布不均匀、误匹配率高导致背景补偿效果不佳的问题,采用小波变换及图像分块处理保证提取的特征点数目及均匀分布,通过SURF算法提取具有尺度不变性的特征点并构建ORB描述子。利用KNN算法与对称约束相结合的特征匹配法提高匹配精度,同时引入改进的RANSAC方法精确求解全局运动参数完成背景运动补偿,最后通过帧差法及形态学处理获得完整的运动目标,并将KCF算法融合Kalman滤波实现对被遮挡目标进行再跟踪。实验结果表明:该方法特征匹配正确率达到98.4%,在背景运动状态下能够实时准确地检测出运动目标,并且能够连续稳定地跟踪目标。

基于激光跟踪小空间搜索的运动轨迹生成方法

以实时跟踪监控多个运动目标为出发点,提出基于激光跟踪小空间搜索的运动轨迹生成方法。激光跟踪测距仪以采集到的激光点和运动目标像素坐标为依据,构建激光自主瞄准模式瞄准运动目标,引导激光靠近运动目标并缩短二者间距离,利用激光跟踪测距仪的小空间搜索性能确定运动目标质心,实现运动目标精准定位检测;采用均值漂移粒子滤波跟踪算法,以检测所得的运动目标位置作为初始粒子跟踪运动目标,获取运动目标跟踪效果图;结合轨迹生成模块,生成运动目标轨迹。实际应用结果表明,检测及跟踪多个运动目标时,准确性高,且生成运动目标轨迹完整且与实际运动轨迹一致,生成结果准确无遗漏可达100%,检测目标车辆的时间均在2.5 s以下,分别低于文献方法2.5 s和1.9 s,降低了检测时间,并且具备性能稳定、实用性强优势。

反无人机视觉检测与跟踪技术进展分析

为应对无人机“黑飞”“滥飞”等对国防和公共安全造成的巨大威胁,反无人机技术研究成为当前迫切的现实需求。首先,对比分析雷达、无线电、声音、机器视觉4类典型的反无人机检测技术;其次,重点针对反无人机的视觉检测与跟踪技术,从目标检测、无人机识别、无人机跟踪等角度,详细分析视觉检测与跟踪关键技术的优势与不足,以及各项技术在反无人机检测跟踪中的应用情况及改进策略;最后,探讨应用中较为突出的检测精度、跟踪遮挡、实时性、数据集收集标准、多技术融合5个方面问题和发展趋势,为相关技术研究提供参考。

机载多通道广角凝视SAR地面动目标指示技术研究

机载广角凝视合成孔径雷达(WasSAR)是一种可对观测区域实施多角度长时间凝视成像探测的新兴SAR成像技术。将机载WasSAR成像与地面运动目标指示(GMTI)技术相结合,则可对重点区域内出现的地面运动目标实施持续成像跟踪监视,从而获取准确的动态感知信息。该文首先建立了机载多通道WasSAR动目标回波模型,分析了WasSAR动目标特性;然后,通过采用偏移相位校正和改进二维自适应校正方法,消除了载机姿态误差与通道非均衡的影响;在此基础上,提出了机载多通道Was SAR动目标检测跟踪算法,实现了复杂路况上行驶动目标的准确检测与跟踪;最后,提出了机载多通道WasSAR动目标行驶轨迹重构算法,实现了起伏路面下的动目标行驶轨迹精确重构。此外,该文中给出了作者团队利用自主研制机载多通道WasSAR-GMTI系统开展的外场飞行试验和实测数据处理结果,验证了地面运动目标持续跟踪监视的有效性和实用性,为后续开展更加深入的研究提供基础。

运动弱小目标先跟踪后检测技术的研究进展

对低信噪比下图像序列运动小目标先跟踪后检测技术进行了较为系统地研究。首先针对运动弱小目标检测问题,分析比较了先检测后跟踪技术与先跟踪后检测技术;详细阐述了先跟踪后检测技术的主流算法,包括时空域匹配滤波器、基于投影变换与三维匹配滤波相结合的算法、多阶假设检验、动态规划算法、高阶相关方法、递推贝叶斯滤波等算法,并比较了其中几种算法。

视频序列中面向人的多目标跟踪算法

针对视频序列中人的跟踪问题,提出一种基于运动检测的多目标跟踪算法.跟踪系统由运动目标检测、关联矩阵建立、特殊情况判断及处理以及轨迹关联4部分构成.提出一种基于改进的c-均值聚类的自适应运动分割方法;不同情况下建立不同的关联矩阵,以准确判断实际场景状况;对遮挡问题作出处理,在两个目标遮挡不严重的情况下,分别采用均值漂移算法对其进行跟踪.实验结果表明,该算法具有较强的鲁棒性,能有效实现复杂场景下多目标跟踪.

基于特征点的多运动目标跟踪

该文针对智能监控的需求,提出基于特征的多运动目标跟踪算法。通过自适应Marr小波核函数背景建模算法,在冗余离散小波域进行多运动目标识别。运动跟踪采用SIFT特征粒子滤波算法,并采用队列链表法记录多运动目标之间的数据关联,在提高识别准确率的同时降低了运算的复杂度。实际测试表明,该算法对于多运动目标识别跟踪具有更优越的实时性和抗遮挡性,在智能监控领域具有较广泛的应用前景。

一种新的双孔径天线干涉SAR动目标检测方法

提出了一种基于双孔径天线沿航迹向干涉SAR进行动目标检测、测速及定位的新方法 .该方法在分析杂波对消必要性的基础上 ,给出了进行地杂波对消、动目标检测、径向速度分量估计及定位的原理和实现方法 .在恒虚警处理后 ,通过比较杂波对消后的残差图像与原始图像中运动目标和静止目标对消幅度的差异 ,检测出运动目标 .同时 ,可以利用残差图象中杂波的对消特性进行运动目标径向速度的估算以及目标的定位 .这种检测方法具有良好的杂波对消性能 ,能够完成被地面背景杂波掩盖的运动目标的检测、测速及定位 .

基于状态依赖检测的视频运动目标轨迹跟踪算法

目前大多数的目标跟踪算法都假设目标检测的概率与轨迹的状态无关,但是随着目标跟踪性能的降低,这个假设通常不正确,针对此问题,提出一种基于状态检测的目标运动轨迹跟踪算法。不同于大多数现有算法,该算法在目标运动轨迹与检测状态相关的基础上展开。首先预测从先前时刻到当前时刻的每个跟踪的概率密度函数;然后从测量值集合中选取一个子集用于更新,并计算相似度测量;最后迭代计算跟踪质量和目标轨迹评估,将目标检测的状态依赖概率融入到多目标跟踪器中。利用二维多目标跟踪进行仿真实验,交替固定传感器每秒都对监控区域进行扫描,结果显示,跟踪确认率可达97%,显著提高了跟踪性能。此外,该算法在其他交通视频上也有较好的跟踪效果,具有较好的实用性。

移动背景下运动目标检测与跟踪技术研究

文中分析了移动背景下运动目标检测与跟踪技术。介绍当前移动背景下运动目标检测常用的方法,指出了难点与关键。结合目前的工作,给出了一个移动背景下运动目标检测与跟踪的解决方案,并以人物为跟踪目标进行了实验,较好地实现了移动背景下的全局运动参数鲁棒性估计以及人物目标在不同场景下的跟踪。但是在目标检测时图像边缘白色块的去除和跟踪时目标遮挡的阈值自适应调节等方面有待进一步研究改进。

运动背景下弱小目标的提取算法

对低信噪比运动背景下序列图像中弱小目标的提取算法进行了研究,提出一种新的目标提取算法。根据运动背景的相关性和目标运动的连续性,采用背景模板匹配技术,计算各帧背景运动量,去除复杂背景,用自适应预估和阈值分割技术捕获跟踪目标。实验中,结合所研究的算法,对100帧包含低对比度弱小目标的运动图像序列进行了处理。实验结果表明,此方法能有效地实现目标的自动捕获与跟踪,跟踪效率高,跟踪稳定。

一种实用的运动目标检测和跟踪算法

为使智能视频监控系统在监控时不仅具有较高的准确率,同时也保证系统的实时性,设计并实现了一种实用的在摄像机静止条件下运动目标检测和跟踪算法。首先对采集到的视频图像进行预处理,为算法提供最佳的图像数据,然后用混合高斯背景建模方法实现前景/背景分割,用形态学操作提取前景目标,最后用基于空间距离的方法实现目标跟踪。算法应用到实验系统,实验表明,系统在保证实时性的同时,能够正确检测和跟踪监控场景中的运动目标。

视频图像处理在输电线路安防系统的应用

介绍了一种基于视频图像差异化分析的输电线路安防系统。图像采集模块通过摄像机捕获和监视场景的光学图像,经模数转换输出数字视频图像,为视频图像处理提供数据。后台专家软件通过调用视频差异化算法对采集的图像进行分析处理,利用边缘检测、特征提取、背景建模、目标跟踪和背景差分等图像处理和模式识别的方法,自动识别出输电线路的多种安全隐患,如大型机械靠近作业、飘撞物、导线覆冰、大风天气产生的导线舞动以及在高负荷状态时出现的弧垂等,并产生预警信号。实验结果表明算法可在多种室外环境下工作,满足实际应用的要求。最后指出复杂环境下的输电线路图像识别算法是进一步研究的方向。

基于DSP的运动目标检测与跟踪系统设计

运动目标检测与跟踪是智能视频监控系统的重要组成部分,其主要功能为检测监控场景中的运动物体,分析其运动轨迹,为高级运动分析提供必要的信息.随着人们对社会安全水平和设备智能化程度要求的提高,运动目标检测与跟踪必将具有广泛的应用前景.针对实时运动目标检测与跟踪,提出了一种基于DSP的运动目标检测与跟踪系统的设计方案,给出了系统总体结构框图,分析了系统工作原理.实现了一种采用结合帧间差分和背景减除的运动物体检测方法,以及一种应用Kalman滤波的运动目标跟踪方法.同时,给出的实验结果表明,该系统能够检测并跟踪特定场合的运动目标.

基于关联帧差分法的运动目标检测与跟踪

针对帧间差分运动目标检测方法检测出的运动目标容易产生拖影现象及存在一定程度的空洞等缺陷,该文提出基于关联帧差分法的运动目标检测与跟踪方法。通过计算相邻三帧图像的相似度,并求取相邻帧之间的差分图像,对差分图像进行与操作后进行轮廓填充来获取运动目标。实验结果表明,该文所提算法不仅可以精确检测与跟踪运动目标,克服帧间差分运动目标检测方法存在的缺陷,还可以检测和跟踪侵入运动目标及消失运动目标。