多场景视频监控中的人物连续跟踪

针对多场景监控,提出一种能在非重叠的多摄像机之间,实时地检测与被跟踪行人（目标）的视频监控系统。该系统分别对每个摄像机视频进行背景建模、前景检测以及运动目标的特征提取,当目标离开摄像机视域的时候,根据已知的拓扑关系向相关摄像机发布监控任务,当有目标进入处于有效监控状态的摄像机视域时,进行目标匹配,从而实现在多摄像机系统中对行人目标的持续跟踪。实验表明,该系统在非重叠摄像机场景中能实现实时的、鲁棒的目标跟踪。     

基于深度学习的多旋翼无人机单目视觉目标定位追踪方法

针对无人机对目标的识别定位与跟踪,本文提出了一种基于深度学习的多旋翼无人机单目视觉目标识别跟踪方法,解决了传统的基于双目摄像机成本过高以及在复杂环境下识别准确率较低的问题。该方法基于深度学习卷积神经网络的目标检测算法,使用该算法对目标进行模型训练,将训练好的模型加载到搭载ROS的机载电脑。机载电脑外接单目摄像机,单目摄像头检测目标后,自动检测出目标在图像中的位置,通过采用一种基于坐标求差的优化算法进行目标位置准确获取,然后将目标位置信息转化为控制无人机飞行的期望速度和高度发送给飞控板,飞控板接收到机载电脑发送的跟踪指令,实现对目标物体的跟踪。试验结果验证了该方法可以很好的进行目标识别并实现目标追踪     

多摄像机目标跟踪系统综述

介绍国内外对多摄像机的研究现状;描述了多摄像机目标跟踪系统的各种构架并进行分析比较;详细阐述各功能模块,分别就算法研究现状、存在的问题及发展趋势进行了分析和总结;最后对研究前景进行展望.     

多摄像机目标跟踪系统设计

针对目前大多数基于视频的目标检测、跟踪系统中,仅对单摄像机视频进行目标分析时存在视野有限、无法处理目标遮挡等问题,搭建了一个基于多摄像机的目标检测与跟踪系统,并给出了系统的总体设计方案;详细阐述了多摄像机系统主要模块如视频采集、目标检测与跟踪、多机通讯、数据管理和实时用户交互等的设计思路,针对多摄像机之间的目标交接、云台控制、多机实时通讯、数据编码等关键技术进行了深入分析研究,提出了相应的解决方法,实现了多摄像机之间的目标交接、连续检测与跟踪等功能。     

双目视觉测量系统的标定及3维测量

针对结构光形位公差视觉测量系统,提出了一阶径向畸变的摄像机成像模型和标定方法,并对传统的直接线性变换(DLT变换)标定算法做了些改进。即先针对没有畸变的线性模型,利用传统的标定算法,解线性超定方程组求解摄像机的全部参数,再针对引入一阶径向畸变的成像模型,以线性模型的参数为初值,通过非线性迭代优化摄像机的图像中心,等效焦距,倾斜因子,畸变系数等内部参数。实验结果表明,该方法无需预标定,精度适中,是相对简单实用的标定方法。     

OPTICS聚类与目标区域概率模型的多运动目标跟踪

目的 针对多运动目标在移动背景情况下跟踪性能下降和准确度不高的问题,本文提出了一种基于OPTICS聚类与目标区域概率模型的方法。方法 首先引入了Harris-Sift特征点检测,完成相邻帧特征点匹配,提高了特征点跟踪精度和鲁棒性;再根据各运动目标与背景运动向量不同这一点,引入了改进后的OPTICS加注算法,在构建的光流图上聚类,从而准确的分离出背景,得到各运动目标的估计区域;对每个运动目标建立一个独立的目标区域概率模型(OPM),随着检测帧数的迭代更新,以得到运动目标的准确区域。结果 多运动目标在移动背景情况下跟踪性能下降和准确度不高的问题通过本文方法得到了很好地解决,Harris-Sift特征点提取、匹配时间仅为Sift特征的17%。在室外复杂环境下,本文方法的平均准确率比传统背景补偿方法高出14%,本文方法能从移动背景中准确分离出运动目标。结论 实验结果表明,该算法能满足实时要求,能够准确分离出运动目标区域和背景区域,且对相机运动、旋转,场景亮度变化等影响因素具有较强的鲁棒性。     

基于目标多区域分割的抗干扰跟踪算法研究

针对视频序列中运动目标跟踪过程中可能出现的目标旋转、遮挡、形变等原因造成的跟踪失败问题,提出了一种基于目标多区域分割的跟踪方法。主要通过将目标划分为多个部分相互重叠的区域,然后选择跟踪过程中相对稳定的多个区域进行定位,进而对跟踪的目标采用不同目标区域权重更新不同的模板更新策略,这样选择主要可以增加算法的抗遮挡、抗旋转能力。实验结果表明,该方法对目标遮挡、旋转等具有一定的适应能力。     

基于粒子滤波和多示例学习的目标跟踪

目标跟踪是计算机视觉领域中研究的热点问题。当前,基于多示例学习的目标跟踪算法引起了较多的关注。在研究多示例学习算法的基础上,针对原始的多示例学习目标跟踪算法中使用运动模型的不足,提出一种改进的基于在线学习的目标跟踪方法。该方法首先根据方向直方图局部特征(HOG特征)来描述目标,然后通过粒子滤波方法对目标位置进行预测,再用基于Boosting的在线多示例学习方法来建立描述目标的模型和分类器,最后在下一帧的图像中利用该分类器来跟踪目标,同时在线更新分类器。通过实验表明,改进的方法可以有效地提高目标跟踪精度和算法的鲁棒性。     

基于差分图象的多运动目标的检测与跟踪

运动目标的检测与跟踪在许多领域有广泛的应用,它是应用视觉研究的焦点之一。     

Constructing a 3D individualized head model from two orthogonal views

A new scheme for constructing a 3D individualized head model automatically from only a side view and the front view of the face is presented. The approach instantiates a generic 3D head model based on a set of the individual's facial features extracted by a local maximum-curvature tracking (LMCT) algorithm that we have developed. A distortion vector field that deforms the generic model to that of the individual is computed by correspondence matching and interpolation. The input of the two facial images are blended and texture-mapped onto the 3D head model. Arbitrary views of a person can be generated from two orthogonal images and can be implemented efficiently on a low-cost, PC-based platform.     

Construction of 3D solid objects from orthographic views

An algorithm to generate three-dimensional solid objects, made up of planar surfaces, from the given three conventional engineering orthographic views is presented in this paper. Consisting of six major steps, the algorithm has been programmed in C on IRIS 1400 graphics workstation. The algorithm generates all possible solutions. The infinite space has been divided into finite subspaces by making use of the surface normals and the direction of travel of the edges that connect the faces. Classification of the probable 3D subobjects into the certain and uncertain ones has proved to be very useful in reducing the time taken by the algorithm. Several illustrative examples, simple as well as complex giving single and multiple solutions, are included.     

基于视频的三维人体运动跟踪系统的设计与实现

在优化粒子滤波跟踪框架下,设计并实现了一个结合多种图像特征、在多摄像机环境下跟踪人体运动的三维人体运动跟踪系统.通过定义三维人体模型、摄像机模型以及观测似然模型,得到跟踪所需目标函数,并使用优化粒子滤波算法进行求解.实验结果表明,该系统能够对人体运动进行准确的跟踪和三维重建,可应用于体育运动分析和动画制作等领域.     

Real-time tracking of multiple objects in space-variant vision based on magnocellular visual pathway

In this paper, we propose a space-variant image representation model based on properties of magnocellular visual pathway, which perform motion analysis, in human retina. Then, we present an algorithm for the tracking of multiple objects in the proposed space-variant model. The proposed space-variant model has two effective image representations for object recognition and motion analysis, respectively. Each image representation is based on properties of two types of ganglion cell, which are the beginning of two basic visual pathways; one is parvocellular and the other is magnocellular. Through this model, we can get the efficient data reduction capability with no great loss of important information. And, the proposed multiple objects tracking method is restricted in space-variant image. Typically, an object-tracking algorithm consists of several processes such as detection, prediction, matching, and updating. In particular, the matching process plays an important role in multiple objects tracking. In traditional vision, the matching process is simple when the target objects are rigid. In space-variant vision, however, it is very complicated although the target is rigid, because there may be deformation of an object region in the space-variant coordinate system when the target moves to another position. Therefore, we propose a deformation formula in order to solve the matching problem in space-variant vision. By solving this problem, we can efficiently implement multiple objects tracking in space-variant vision.     

Rectification of 3D data obtained from moving range sensor by using extended projective multiple view geometry

To measure the 3D shape of large objects, scanning by a moving range sensor is one of the most efficient methods. However, if we use moving range sensors, the obtained data have some distortions due to the movement of the sensor during the scanning process. In this paper, we propose a method for recovering correct 3D range data from a moving range sensor by using the multiple view geometry under projective projections in space-time. We assume that range sensor radiates laser beams in a raster scan order, and they are observed from two cameras. We first show that we can deal with range data as 2D images, and show that the extended multiple view geometry can be used for representing the relationship between the 2D image of range data and the 2D image of cameras. We next show that the extended multiple view geometry can be used for rectifying 3D data obtained by the moving range sensor. The method is implemented and tested in synthetic images and range data. The stability of the recovered 3D shape is also evaluated.     

基于Matlab实时运动目标跟踪检测系统

运动目标的检测跟踪是视频理解技术和计算机视觉的研究热点,其在解决智能视频监控,人机交互,智能交通系统等领域有着广泛而重要的应用,基于此利用matlab平台构建出USB摄像头实时图像采集处理系,从视频流采集到处理综合利用了背景估测,图像分割,目标检测与跟踪算法准确高效地检测出环境场景中的动态目标,并成功地对其进行实时追踪。利用matlab的simulink模块编程实现提取视频流YcbCr输入系统进行运算处理,并改进了背景估测和目标检测算法,提高系统的实时性。最终利用多次试验,对室内和室外运动目标实现检测跟踪,验证了系统处理实际问题的可靠性能。     

增强现实中三维跟踪注册技术概述

三维跟踪注册技术是增强现实领域中的重要关键技术，通过对现实场景中的图像或物体进行跟踪与定位，将虚拟物体按照正确的空间透视关系叠加到真实场景当中。综述了增强现实中的三维跟踪注册技术（基于计算机视觉的跟踪注册技术、基于硬件传感器的跟踪注册技术以及混合跟踪注册技术），详细阐述了不同跟踪注册技术的优势与不足，并介绍了不同跟踪注册技术的当前应用现状，随后探讨了跟踪注册技术的发展趋势与存在的难题，对三维跟踪注册技术的进一步研究进行了展望。     

A family of multiopoint iterative functions for finding multiple roots of equations

In this paper, we have derived a family of multipoint iterative functions for finding multiple roots of equations. In addition, comparison of computational results are made with other well-known methods.