体育视频中的运动目标智能跟踪研究

视频运动目标跟踪广泛应用于体育领域中,为了提高运动目标的跟踪精度,提出一种体育视频中的运动目标智能跟踪方法。首先收集体育视频,考虑体育视频中运动目标的多样性,采用改进背景更新差分法对运动目标进行检测,然后采用卡尔曼滤波算法对运动目标进行跟踪,最后对多个体育视频运动目标进行跟踪实验。结果表明,该方法可以准确、快速地对体育视频中的运动目标进行跟踪,而且运动目标跟踪的实时性要优于其他跟踪方法,为体育视频中的运动目标跟踪提供了一种新的研究工具。     

基于标签多伯努利滤波器的机动小目标检测前跟踪

标签多伯努利(LMB)滤波器在传统多伯努利滤波器基础上引入标签空间,能够实现真正意义上的多目标轨迹级滤波.文章对红外小目标的运动和量测进行建模,将标签多伯努利应用到红外小目标检测前跟踪领域.在此基础上,为了实现对运动模型时变目标的检测前跟踪,将交互式多模型(IMM)与LMB检测前跟踪算法相结合,提出IMM-LMB检测前跟踪算法.此外,给出了该算法的序贯蒙特卡罗实现.仿真结果表明,所提算法能够从输入的原始图像中直接实现轨迹级多目标检测和跟踪,且能够在线更新多模型概率,更好的适应多机动目标场景.     

红外图像中快速运动目标的检测与跟踪方法

红外热成像图像具有分辨率较低,细节模糊,对于快速运动目标适应性较差的特点。本文提出了一种结合目标检测算法,目标跟踪算法的红外图像中快速运动目标的检测与跟踪方法。该方法根据红外图像特点,使用ViBE算法检测运动目标,检测出图像中显著运动目标后,触发跟踪器,使用fDSST目标跟踪算法对显著运动目标进行跟踪。测试结果表明,该方法对于快速运动的红外图像目标能够高效检测、快速跟踪。检测与跟踪效果相对传统方法具有检测率更高、鲁棒性更好、实时性更强的优势,对于红外图像中目标检测与跟踪具有很强应用价值。     

一种改进的自适应运动目标检测算法

针对运动多目标识别与跟踪所必需的准备环节--运动目标检测,本文提出了的一种改进的自适应运动目标检测方法,检测运动目标是否存在,并利用试验证明,该方法确实不仅能有效抑制光照变化、人影、噪声等影响,进而对运动目标是否在跟踪区域徘徊进行判断,保证了对进出目标跟踪的准确跟踪,而且运算量小,容易软件实现,实际效果较好.     

基于改进分水岭算法的多目标车辆的跟踪研究

近年来,由于车辆数量的激增,道路事故也频频发生,这就要对事故易发路段进行监控,并对车辆进行跟踪。针对目标的跟踪,提出了利用改进的分水岭方法与数据关联方法相结合实现多目标车辆准确跟踪,在检测车辆时利用分水岭算法可以有效地进行图像分割并准确的检测出运行车辆;跟踪时利用运动目标轮廓采用链表法记录多运动目标之间的数据关联,并跟据质心特征进行跟踪。实验表明该方法能有效地对目标进行了检测并提高了跟踪的准确率。     

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...     

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。     

基于HALCON的轻量级物体识别方法

<正>实时的运动检测和跟踪技术是一项融入图像处理、计算机视觉、网络传输等多学科的技术，在生物医学、工业自动化生产、智能机器人等多个领域都有着非常广泛的应用。所谓实时的运动检测与跟踪技术就是将实时采集的图像信息进行检测，提取有用信息，识别目标参数特征，过滤噪声并锁定目标^[1]，而实现运动物体的检测和跟踪依赖于对物体的识别，所以利用机器视觉技术对目标进行快速自动地识别别成为了关键。但现在大多数运动检测系统基于PC机等大型且计算能力较强的设备，基于卷积神经网络(CNN)的机器学习算法已经在很多领域中进行了探索和应用^[2]。而某些识别中，     

基于序列图像的运动目标检测与跟踪

序列图像中的运动目标检测与跟踪是计算机视觉研究的主要内容之一,在很多领域有着广泛的应用.介绍了一种基于背景减除法的目标检测算法,然后通过对前几帧中目标的质心用逼近函数预测出目标在下一帧中的位置.试验结果表明,该方法快速、有效,能够满足运动目标的检测与跟踪要求.     

运动目标跟踪技术研究

目标跟踪已经渗透到公安、军事等越来越多的领域,甚至是娱乐应用,开始逐渐进入到家庭中.本文主要介绍运动目标跟踪的概论、运动模板的原理、利用运动模板来捕捉边缘,从而跟踪目标的运动,并通过实验演示了跟踪效果.对运动目标跟踪技术研究具有十分重要的理论意义和应用价值.     

Moving targets pattern recognition based on the wavelet neural network

Based on pattern recognition theory and neural network technology, moving objects automatic detection and classification method integrating advanced wavelet analysis are discussed in detail. An algorithm of moving targets pattern recognition on the combination of inter-frame difference and wavelet neural network is presented. The experimental results indicate that the designed BP wavelet network using this algorithm can recognize and classify moving targets rapidly and effectively.     

基于WVD-HT的SAR/ISAR多运动目标检测

本文提出和研究了基于Wigner-Ville分布(WVD)和Hough变换(HT)的合成孔径雷达(SAR)和逆合成孔径雷达(ISAR)对多运动目标成象的运动补偿新方法,给出了多运动目标检测、参数估算和运动补偿的主要步骤。理论分析、计算机和微波暗室模拟实验的结果表明,新方法对交叉项和噪声的抑制是有效的,它与单一的WVD法相比,提高了在噪声中对单个和多个运动目标检测及其参数估算的能力。     

Detecting moving object from dynamic background video sequences via simulating heat conduction

Moving object detection is one of the essential tasks for surveillance video analysis. The dynamic background often composed by waving trees, rippling water or fountains, etc. in nature scene greatly interferes with the detection of moving objects in the form of noise. In this paper, a method simulating heat conduction is proposed to extract moving objects from dynamic background video sequences. Based on the visual background extractor (ViBe) with an adaptable distance threshold, we design a temperature field relying on the generated mask image to distinguish between the moving objects and the noise caused by dynamic background. In temperature field, a brighter pixel is associated with more energy. It will transfer a certain amount of energy to its neighboring darker pixels. Through multiple steps of energy transfer the noise regions loss more energy so that they become darker than the detected moving objects. After heat conduction, K-Means algorithm with the customized initial clustering centers is utilized to separate the moving objects from background. We test our method on many videos with dynamic background from public datasets. The results show that the proposed method is feasible and effective for moving object detection from dynamic background sequences.     

多运动目标双向正交多级分割技术研究

介绍了以时空差分、数据融合和模糊聚类来估计动目标边界点集的技术。为了解决检测所得多目标边界的非闭合问题，提出了一种双向正交多级分割技术。利用空间最小距离准则，实现了各目标边界点集的划分。PC试验说明，在满足最小距离的条件下，该技术能够迅速有效地提取出视频序列中的运动对象。     

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.     

Moving object detection and tracking from video captured by moving camera

This paper presents an effective method for the detection and tracking of multiple moving objects from a video sequence captured by a moving camera without additional sensors. Moving object detection is relatively difficult for video captured by a moving camera, since camera motion and object motion are mixed. In the proposed method, the feature points in the frames are found and then classified as belonging to foreground or background features. Next, moving object regions are obtained using an integration scheme based on foreground feature points and foreground regions, which are obtained using an image difference scheme. Then, a compensation scheme based on the motion history of the continuous motion contours obtained from three consecutive frames is applied to increase the regions of moving objects. Moving objects are detected using a refinement scheme and a minimum bounding box. Finally, moving object tracking is achieved using a Kalman filter based on the center of gravity of a moving object region in the minimum bounding box. Experimental results show that the proposed method has good performance.     

Motion detection using the randomised Hough transform: exploitinggradient information and detecting multiple moving objects

An algorithm to detect one moving object using the randomised Hough transform (RHT) has previously been proposed. This new nonmodel-based method, called motion detection using randomised Hough transform (MDRHT), was shown be to applicable to translational and rotational motion detection of one moving object. The basic, earlier version of the MDRHT utilises edge points only as its features. The MDRHT is extended to use both edge pixels and the intensity-gradient vector at edge pixels. Moreover, the MDRHT method is generalised to detect also multiple moving objects. The translational motion experiments with the variant of the technique using gradient information and coping with several moving objects give promising results in two-dimensional-motion detection and estimation, compared with the earlier version of the MDRHT     

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。     

基于OpenCV的视频对象的运动检测

随着信息技术的发展和视频监控系统的普及,视觉监控技术在科学研究、工业生产中得到了越来越多的应用。智能监控系统包括3方面内容：运动检测、运动方向判定和图像跟踪,其中运动检测是重要的基础内容。本文介绍了一种以视频图像为检测源的快速运动的检测方法,与传统的运动检测方法相比较,该方法采用了视频处理技术,通过先进的运动检测算法快速识别运动物体,并能准确、迅速判断出运动物体的运动方向,并予以标注。实验表明该方法是有效的。     

A lightweight multi-scale aggregated model for detecting aerial images captured by UAVs

Detecting the objects of interesting from aerial images captured by UAVs is one of the core modules in the UAV-based applications. However, it is very difficult to detection objects from aerial images. The reason is that the scale of objects in the aerial images captured by UAVs varies greatly and needs to meet certain real-time performance in detection. To deal with these challenges, we proposed a lightweight model named DSYolov3. We made the following improvements to the Yolov3 model: 1) multiple scale-aware decision discrimination network to detect objects in different scales, 2) a multi-scale fusion-based channel attention model to exploit the channel-wise information complementation, 3) a sparsity-based channel pruning to compress the model. Extensive experimental evaluation has demonstrated the effectiveness and efficiency of our approach. By the proposed approach, we could not only achieve better performance than most existing detectors but also ensure the models practicable on the UAVs.