Target detection algorithm for basketball moving images based on improved gaussian mixture model

The various methods used to detect and monitor basketball are explored in detail. Object detection and tracking and video Object detection and tracking are realized in a high-performance proportional manner. But most methods suffer from difficult predictions. The problematic reduction can occur at any stage, such as preprocessing, disassembly, feature extraction, background subtraction, and hole-filled ball tracking. In this article, the ball detection and tracking method based on the study method path is the growing method in the region, the public method, and the average transformation method using HSV color space and system features. Extensive inquiries into strategies, implementation issues, and future development trends. This research, edge detection, grayscale processing, object capture, target recognition, image detection technology, etc. are integrated into the game video's actual need to achieve the various requirements of motion image detection. At the same time, it has established a testing ground for research players, sports recognition, sports behavior judgment, etc. Background subtraction is a typical computer vision for Jobs. Methods examined Pixel is commonly used. Develop practical adaptive algorithms. Use a Gaussian probability density mixture. The recursive formula is used. Choose the appropriate number of parameters that are continuously updated and regions Per Pixel. The image data normalization and space-based communication planning and coordination of the work authorization model of small l is the static representation.     

运动捕捉数据中足迹的谱聚类检测方法

目的 从运动捕捉数据中直接检测足迹非常困难。目前,已有的方法不能在无人工交互条件下实现对任意平地运动数据的足迹自动检测,为此提出一种基于谱聚类的足迹自动检测方法,可应用于任意角色的平地运动。方法 首先,提取角色的脚部运动特征并表示为样本。然后,分析样本模长的变化规律并自适应计算谱聚类参数。最后,使用谱聚类方法检测出足迹帧。结果 实验应用于混合运动数据集后,足迹检测的准确性良好,检测的整体准确率可达98.72%。结论 对实验结果的分析以及与基准线法的比较,证明了本文方法的普适性和有效性。     

Silk road dance exhibition system based on motion capture

Silk Road dance is an excellent traditional Chinese dance drama, which has high artistic values. This article introduces the virtual performance techniques of Silk Road based on motion capture. First, obtain the dance data using motion capture technique according to the features of dance itself, then establish figure model, finally combines these data to make 3-D animation system to perform Silk Road, meanwhile designs some interfaces to introduce it. This system excavates details of the dance, and can be reference of man-machine interaction and animation production.     

基于PGA的人体运动捕获数据分割方法

由于人体运动捕获数据的固有非线性,线性方法并不总是能够有效地找到运动捕获数据的内在维度,针对这种情况,提出了基于主测地线分析（PGA）和概率主测地线分析（PPGA）的自动分割方法。这两种方法都将人体运动视为一个有序的姿势序列,并在姿势序列有局部变化处对运动进行分割。基于PGA的分割方法是在运动局部模型的内在维度突然增长处分配一个分割点,基于PPGA的分割方法是在姿势分布发生改变时放置分割点。实验结果表明,该方法都能实现自动分割,且具有较好的分割结果。     

基于运动捕获数据的虚拟人动画研究

随着三维游戏等行业对计算机动画制作需求的增加,在三维动画制作软件中人工调整虚拟人动作的工作方式已经不再适合现在的计算机动画制作。运动捕获技术是直接记录物体的运动数据并将其用于生成计算机动画,具有高效率、所生成的动画真实感强等优点,因而获得了广泛应用。提出了一种利用运动捕获数据来生成动画的方法,基于运动捕获得到的数据建立并驱动三维骨架模型,从而产生骨架的运动,形成动画。该方法可以充分利用现有的大量运动捕获数据,因此具有较大的应用前景。     

无线传感器网络中基于移动智能体的目标追踪

就无线传感器网络中的目标追踪问题而言,减少传感器之间的冲突、提高追踪精度是两个重要问题．为此,采用移动智能体方法协同声音传感器追踪移动目标．当目标出现时,负责追踪的智能体产生,并随目标移动而移动．为减小在选举移动智能体的过程中出现的冲突,采用基于概率的选举策略,给出概率函数的设置方法．在目标关联过程中,时间窗口的大小对算法的追踪精度而言至关重要,经过分析,给出了设置时间窗口大小的基本原则．仿真结果表明设计恰当的随机函数和时间窗口可以有效地减小冲突、提高追踪的精度．     

Fast moving pedestrian detection based on motion segmentation and new motion features

The detection of moving pedestrians is of major importance for intelligent vehicles, since information about such persons and their tracks should be incorporated into reliable collision avoidance algorithms. In this paper, we propose a new approach to detect moving pedestrians aided by motion analysis. Our main contribution is to use motion information in two ways: on the one hand we localize blobs of moving objects for regions of interest (ROIs) selection by segmentation of an optical flow field in a pre-processing step, so as to significantly reduce the number of detection windows needed to be evaluated by a subsequent people classifier, resulting in a fast method suitable for real-time systems. On the other hand we designed a novel kind of features called Motion Self Difference (MSD) features as a complement to single image appearance features, e. g. Histograms of Oriented Gradients (HOG), to improve distinctness and thus classifier performance. Furthermore, we integrate our novel features in a two-layer classification scheme combining a HOG+Support Vector Machines (SVM) and a MSD+SVM detector. Experimental results on the Daimler mono moving pedestrian detection benchmark show that our approach obtains a log-average miss rate of 36 % in the FPPI range [10^?2,10⁰], which is a clear improvement with respect to the naive HOG+SVM approach and better than several other state-of-the-art detectors. Moreover, our approach also reduces runtime per frame by an order of magnitude.     

Target detection algorithm and data model optimization based on improved Gaussian mixture model

A set of multi-stage image processing algorithms developed to detect the change of image sequence analysis. This means Moving Target Identification (MTI). The difference compared with the traditional method, the new method of spatial diversity. General purpose of object detection, image processing and image understanding of the specific field of research. Hue Saturation and Value (HSV,) colour space algorithm uses spot and shape detection to ensure detection under various conditions of lighting, shade, and distance. The algorithm is tested on the lighting and form detection account where the different variation of the displays. The only computational process of the challenge group is the presence of more than one target of the same colour in finding the correct target under changing lighting conditions. It has been found that the elasticity target people based on localization of these image processing methods for better detection is compared with the target detection. Digital image data contains most of this image data recognition model is optimized by integrating task planning normalization and inertial representation of the remote sensing image classification model based on spatial communication. The immediate message issue is that the Gaussian compound model cannot detect the entire moving object, and is prone to sudden light changes, etc. The advanced algorithm to be performed is proposed based on the Gaussian compound model and the detection method of the three legal difference moving object. Subsequently, a new adaptive selection technique for Gaussian distributions is introduced to reduce processing time and improve detection accuracy.     

无线传感器网络信息检测的目标跟踪算法

目标跟踪定位算法通过簇头节点与汇聚节点之间的通信来定位预测目标位置,但在信息传递过程中会因中间路由出错或者外部攻击致使到达汇聚节点的信息出现错误或丢失,影响目标定位预测的精度。通过分析路由过程中可能产生误差的原因(例如乱序和丢包)以及受攻击之后数据的特征采取有效策略,在汇聚节点接收到信息之后进行检测,排除异常数据,防止错误信息的干扰。仿真实验表明,在非理想状态下加入信息检测机制之后相对于无检测机制的情况定位精度提高,跟踪轨迹更加精确。     

Human motion capture data retrieval based on semantic thumbnail

We present a method for the efficient retrieval and browsing of immense amounts of realistic 3D human body motion capture data. The proposed method organizes motion capture data based on statistical K-means (SK–means), democratic decision making, unsupervised learning, and visual key frame extraction, thus achieving intuitive retrieval by browsing thumbnails of semantic key frames. We apply three steps for the efficient retrieval of motion capture data. The first is obtaining the basic type clusters by clustering motion capture data using the novel SK-means algorithm, and after which, immediately performing character matching. The second is learning the retrieval information of users during the retrieval process and updating the successful retrieval rate of each data; the search results are then ranked on the basis of successful retrieval rate by democratic decision making to improve accuracy. The last step is generating thumbnails with semantic generalization, which is conducted by using a novel key frame extraction algorithm based on visualized data analysis. The experiment demonstrates that this method can be utilised for the efficient organization and retrieval of enormous motion capture data.     

基于pHash分块局部探测的海量图像查重算法

数据库中大量重复图片的存在不仅影响学习器性能,而且耗费大量存储空间。针对海量图片去重,提出一种基于pHash分块局部探测的海量图像查重算法。首先,生成所有图片的pHash值;其次,将pHash值划分成若干等长的部分,若两张图片的某一个pHash部分的值一致,则这两张图片可能是重复的;最后,探讨了图片重复的传递性问题,针对传递和非传递两种情况分别进行了算法实现。实验结果表明,所提算法在处理海量图片时具有非常高的效率,在设定相似度阈值为13的条件下,传递性算法对近30万张图片的查重仅需2 min,准确率达到了53%。     

基于AMR传感器的车辆检测算法

深入研究了各向异性磁阻(AMR)传感器的数据采集原理和特征波形向量提取方法,提出了一种基于AMR传感器的车辆检测算法:自适应状态机的车辆检测算法。该算法可以自适应地更新阈值和基线,利用状态机,达到对车辆的准确和高效的检测。可以用于检测道路交通车流量,也可以应用在大型停车场的车辆诱导系统。     

基于运动目标检测的视频水印算法研究

摘要：为了提高视频水印的鲁棒性,提出一种基于运动目标检测技术的算法。通过相邻帧差法提取并标记视频图像序列中的运动目标,并采用图像局部奇异值分解(SVD)算法,实现水印的嵌入和盲提取过程。在仿真实验中,通过计算水印嵌入后图像的峰值信噪比,证明该水印算法具有很好的不可见性和隐蔽性;并使用strimark软件对嵌入水印后图像进行几何攻击,分析水印图像的相关系数,验证本算法具有很好的鲁棒性。     

基于隐空间的运动捕获数据自动分割*

提出一种运动捕获数据自动分割方法。利用高斯过程隐变量模型对运动捕获数据进行降维,将其从高维观察空间映射到低维隐空间;在隐空间中构造运动特征函数,该函数具有结构简单、对所有关节敏感等优点,通过分析运动特征函数几何特征的变化,探测运动捕获数据的分割点,实现运动自动分割。实验结果表明,该方法具有较高的准确度和较好的普适性。     

基于区域生长的SAR图像目标检测方法研究

基于区域生长算法和SAR图像相干斑的特点,提出了一种有效的SAR图像目标提取方法。该方法结合SAR图像的目标特征,分析了区域生长的关键问题（种子点的选取和相似性生长准则）并提出相应的决策,给出了实现SAR图像中目标的提取步骤。仿真结果表明该方法具有较强的抗噪性能和应用潜力。     

基于谱残差和聚类法的运动目标检测研究

传统基于特征点匹配的目标检测算法目标识别率低、误检率较高是因为特征点匹配不准确、目标轮廓不连续。针对这一问题,分别引入谱残差算法和k means聚类算法,并加以改进,提出一种基于谱残差算法和k means聚类算法的运动目标检测算法。具体方法是：首先,每隔两帧提取加速鲁棒特征SURF并对图像配准,再对帧差结果采用谱残差算法提取视觉显著性特征,去除因匹配不准确造成的噪点和伪运动目标;其次,形态学处理之后引入改进后的k means聚类算法,对不连续的轮廓进行聚类;最后形成完整的目标。实验显示,本文算法目标识别率达到90.61%,误检率达到21.25%,分别优于传统基于SURF特征的运动目标检测算法66.60%的识别率、31.91%的误检率和基于新的局部不变性特征ORB匹配的目标检测算法87.573%的识别率、26.80%的误检率。虽然该算法平均运行时间为18 fps,但仍可以满足视频流畅的需求,因此动态背景下该算法可做为一种有效的运动目标检测算法使用。     

基于运动显著性的移动镜头下的运动目标检测

针对移动镜头下的运动目标检测中的背景建模复杂、计算量大等问题,提出一种基于运动显著性的移动镜头下的运动目标检测方法,在避免复杂的背景建模的同时实现准确的运动目标检测。该方法通过模拟人类视觉系统的注意机制,分析相机平动时场景中背景和前景的运动特点,计算视频场景的显著性,实现动态场景中运动目标检测。首先,采用光流法提取目标的运动特征,用二维高斯卷积方法抑制背景的运动纹理;然后采用直方图统计衡量运动特征的全局显著性,根据得到的运动显著图提取前景与背景的颜色信息;最后,结合贝叶斯方法对运动显著图进行处理,得到显著运动目标。通用数据库视频上的实验结果表明,所提方法能够在抑制背景运动噪声的同时,突出并准确地检测出场景中的运动目标。     

基于Fisher信息距离的被动传感器目标协同跟踪方法

针对多被动传感器动态跟踪问题,提出了一种基于Fisher信息距离被动传感器目标协同跟踪方法。该算法在进行传感器选择时,依据信息几何理论,以流形中的Fisher信息距离来衡量先验概率密度函数和后验概率密度函数之间的距离,继而以此距离为依据选择传感器进行目标跟踪。仿真实验表明：所提算法能够在动态环境中自适应选择传感器资源,有效提高目标的跟踪精度,实现多被动传感器协同跟踪。     

Ground moving target detection and location based on SAR images for distributed spaceborne SAR

In recent years, a new concept of spaceborne SAR implementation has been propos- ed[1,2], i.e., the distributed (constellation) spaceborne SAR implementation in which sev-eral formation-flying small satellites (or micro-satellites) cooperates to perform multiple missions. The coherent combination of ground echoes acquired by all small satellites can significantly improve the performance of ground moving target indication (GMTI), SAR mapping, interferometric SAR (InSAR), etc. Since the …