基于均值漂移和边缘检测的轮廓跟踪算法

实时的轮廓跟踪算法可以为视频监控系统提供物体的轮廓信息以供对物体类别、物体行为等进行识别.提出一种基于均值漂移和边缘检测的轮廓跟踪算法.方法中,首先利用均值漂移算法跟踪得到目标物体的中心位置,同时用高斯统计模型进行背景更新,从前景图像和背景图像中分别得到具有相同位置和大小的前景矩形区域和背景矩形区域,然后用背景分割的方法得到目标物体区域,再对目标物体区域进行边缘检测就得到了目标物体的轮廓,进而实现了对目标物体的轮廓跟踪.实验表明,可以实时、准确、稳定地对目标物体进行轮廓跟踪.     

视频目标检测匹配跟踪算法优化研究

针对目前视频目标检测匹配跟踪算法不能满足视频监控的高实时性要求,不能满足当前硬件平台主流技术的问题,研究了差分目标检测和匹配跟踪算法的优化实现问题.为优化算法减少计算量,选用了连续帧训练背景的方法,利用背景差分检测出场景中的运动物体,采用模板匹配跟踪算法,将目标检测和跟踪算法在DM642上优化并实现.仿真结果表明,经过算法和程序级的优化,程序运行时间大大减少,可在CIF格式下较好地进行多物体的实时检测与跟踪.     

一种自适应色彩融合的Mean-Shift跟踪算法

针对现有的Mean-Shift算法使用单纯的颜色特征不能适应光线及背景的变化,易受颜色相近物体干扰的问题,提出了自适应色彩融合方法来提高跟踪性能。对背景以极坐标的形式进行不等间隔采样,以融合后的目标直方图与背景直方图具有最小相似性为原则搜索色调与饱和度的最佳线性融合系数;考虑背景与目标的渐变,跟踪过程中在最佳融合系数的自适应调整邻域内调整融合系数;能够有效处理相似物体和颜色相近的大背景带来的干扰。视频序列跟踪结果表明,提出的方法能够实时、稳定地进行跟踪。     

字典学习联合粒子滤波鲁棒跟踪

针对运动目标鲁棒跟踪问题,提出一种基于离线字典学习的视频目标跟踪鲁棒算法。采用字典编码方式提取目标的局部区域描述符,随后通过训练分类器将跟踪问题转化为背景和前景分类问题,最终通过粒子滤波对物体位置进行估计实现跟踪。该算法能够有效解决由于光照变化、背景复杂、快速运动、遮挡产生的跟踪困难。经过不同图像序列的实验对比表明,与现有方法相比,本文算法的鲁棒性较高。     

分布式网络信息数据防篡改方法研究

为在足球视频中有效的检测与跟踪运动目标,需要对足球比赛视频中目标检测与跟踪算法进行研究。当前采用的算法,在动态场景中,存在运动目标检测与跟踪效果不佳的问题。为此,提出一种基于OpenCV的足球比赛视频中目标检测与跟踪算法。该算法结合平均背景算法将足球比赛视频中目标图像分割为前景区与背景区,计算足球比赛视频每一帧目标图像和背景图像之间差值的绝对差值,同时计算每一个目标图像中像素点的平均值与标准值来建立目标图像背景统计模型,利用TMHI算法对足球比赛视频中目标初始图像进行阈值分割,得到初始分割图像,对分割图像进行中值滤波和闭运算,再使用卡尔曼滤波对分割后的目标图像进行处理,得到镜头中目标的质心位置和目标外界矩形框,然后对足球比赛视频中目标进行跟踪。实验证明,该算法有效的检测与跟踪足球视频中运动目标。     

自适应均值漂移算法目标跟踪检测仿真研究

研究运动物体目标跟踪精确度问题,由于存在遮挡和多光源的噪声影响检测精度,而且运动目标的跟踪是在连续的图像帧间创建位置、速度、形状等存在匹配问题。传统的目标跟踪算法由于目标的动态移动速度大,而容易导致跟踪丢失目标。为了解决上述问题,提出了一种改进的基于自适应均值移动(Cam Shift)目标跟踪新算法。主要难点技术问题是提取了多运动目标视频图像,进行了背景分离。算法是一种颜色跟踪算法,根据多次迭代的计算结果,自适应调整图像,实现对运动目标的实时跟踪。仿真结果表明,提出的改进目标跟踪算法的跟踪精度和滤波效果有了较大提高,同时具有较强的鲁棒性能。     

一种自适应色彩融合的Mean Shift跟踪算法*

针对现有的MeanShift算法使用单纯的颜色特征不能适应光线及背景的变化,易受颜色相近物体干扰的问题,提出了自适应色彩融合方法来提高跟踪性能。对背景以极坐标的形式进行不等间隔采样,以融合后的目标直方图与背景直方图具有最小相似性为原则搜索色调与饱和度的最佳线性融合系数;考虑背景与目标的渐变,跟踪过程中在最佳融合系数的自适应调整邻域内调整融合系数;能够有效处理相似物体和颜色相近的大背景带来的干扰。视频序列跟踪结果表明,提出的方法能够实时、稳定地进行跟踪。     

一种基于Hausdorff距离的运动物体跟踪算法

针对视频处理中运动物体的检测和跟踪问题,提出了一种基于Hausdorff距离的目标跟踪算法。新算法提出首先采用多尺度分水岭变换获取运动物体模型,消除了传统基于分水岭变换算法存在的缺陷;然后使用部分Hausdorff距离实现后续帧中运动物体模型的匹配;最后再次使用多尺度分水岭算法完成运动物体模型的更新。实验表明,该算法可以有效地跟踪多个刚体或非刚体目标。     

基于字典学习的粒子滤波鲁棒跟踪算法

字典学习广泛应用于图像去噪、图像分类等领域,但是将离线字典训练如何应用于视频目标跟踪的研究较少。本文采用一种字典编码方法提取目标的局部区域描述符,通过训练分类器将跟踪问题转化为背景和前景二值分类问题,并通过粒子滤波对物体位置进行估计实现跟踪。不同图像序列的实验结果表明,与现有的方法相比本文的算法具有较好的鲁棒性。     

自适应紧致特征的超像素目标跟踪

目的 针对现有的超像素目标跟踪算法（RST）对同一类中分别属于目标和相似干扰物体的超像素块赋予相同特征置信度,导致难以区分目标和相似干扰物的问题,为此提出自适应紧致特征的超像素目标跟踪算法（ACFST）。方法 该方法在每帧的目标搜索区域内构建适合目标大小的自适应紧致搜索区域,并将该区域内外的特征置信度分别保持不变和降低。处于背景中的相似干扰物体会被该方法划分到紧致搜索区域外,其特征置信度被降低。当依据贝叶斯推理框架求出对应最大后验概率的目标时,紧致搜索区域外的特征置信度低,干扰物体归属目标的程度也低,不会被误判为目标。结果 在具有与目标相似干扰物体的两个视频集进行测试,本文ACFST跟踪算法与RST跟踪算法相比,平均中心误差分别缩减到5.4像素和7.5像素,成功率均提高了11%,精确率分别提高了10.6%和21.6%,使得跟踪结果更精确。结论 本文提出构建自适应紧致搜索区域,并通过设置自适应的参数控制紧致搜索区域变化,减少因干扰物体与目标之间相似而带来的误判。在具有相似物体干扰物的视频集上验证了本文算法的有效性,实验结果表明,本文算法在相似干扰物体靠近或与目标部分重叠时,能够保证算法精确地跟踪到目标,提高算法的跟踪精度,具有较强的鲁棒性,使得算法更能适应背景杂乱、目标遮挡、形变等复杂环境。     

Incremental Focus of Attention for Robust Vision-Based Tracking

We present the Incremental Focus of Attention (IFA) architecture for robust, adaptive, real-time motion tracking. IFA systems combine several visual search and vision-based tracking algorithms into a layered hierarchy. The architecture controls the transitions between layers and executes algorithms appropriate to the visual environment at hand: When conditions are good, tracking is accurate and precise; as conditions deteriorate, more robust, yet less accurate algorithms take over; when tracking is lost altogether, layers cooperate to perform a rapid search for the target and continue tracking.Implemented IFA systems are extremely robust to most common types of temporary visual disturbances. They resist minor visual perturbances and recover quickly after full occlusions, illumination changes, major distractions, and target disappearances. Analysis of the algorithm's recovery times are supported by simulation results and experiments on real data. In particular, examples show that recovery times after lost tracking depend primarily on the number of objects visually similar to the target in the field of view.     

单目标跟踪算法的研究与分析

运动目标跟踪是计算机视觉的一个中心研究问题,为视频内容的理解提供重要的信息。首先介绍了目标跟踪的国内外研究现状,重点归纳分析了运动目标跟踪方法的分类及其发展过程中的提出的各种算法,对其关键技术进行了剖析和比较。     

数字化封闭图形对象的面积计算

四方向跟踪算法可以用于计算数字化封闭图形对象的面积,但由于四方向跟踪算法有其自身的不足之处,因此笔者对其作了改进,得到四方向试探跟踪算法和八方向跟踪算法。本文主要是为改进后的算法给出与四方向跟踪算法类似的计算图形对象面积的方法。     

Probabilistic Tracking with Exemplars in a Metric Space

A new, exemplar-based, probabilistic paradigm for visual tracking is presented. Probabilistic mechanisms are attractive because they handle fusion of information, especially temporal fusion, in a principled manner. Exemplars are selected representatives of raw training data, used here to represent probabilistic mixture distributions of object configurations. Their use avoids tedious hand-construction of object models, and problems with changes of topology.Using exemplars in place of a parameterized model poses several challenges, addressed here with what we call the Metric Mixture (M²) approach, which has a number of attractions. Principally, it provides alternatives to standard learning algorithms by allowing the use of metrics that are not embedded in a vector space. Secondly, it uses a noise model that is learned from training data. Lastly, it eliminates any need for an assumption of probabilistic pixelwise independence.Experiments demonstrate the effectiveness of the M² model in two domains: tracking walking people using chamfer distances on binary edge images, and tracking mouth movements by means of a shuffle distance.     

Probabilistic people tracking with appearance models and occlusion classification: The AD-HOC system

AD-HOC (Appearance Driven Human tracking with Occlusion Classification) is a complete framework for multiple people tracking in video surveillance applications in presence of large occlusions. The appearance-based approach allows the estimation of the pixel-wise shape of each tracked person even during the occlusion. This peculiarity can be very useful for higher level processes, such as action recognition or event detection. A first step predicts the position of all the objects in the new frame while a MAP framework provides a solution for best placement. A second step associates each candidate foreground pixel to an object according to mutual object position and color similarity. A novel definition of non-visible regions accounts for the parts of the objects that are not detected in the current frame, classifying them as dynamic, scene or apparent occlusions. Results on surveillance videos are reported, using in-house produced videos and the PETS2006 test set.     

基于PLC的碟式太阳能跟踪控制系统设计

设计了一种以FX3U系列PLC为控制核心的太阳能自动跟踪控制系统。该跟踪控制系统将视日运动轨迹跟踪与传感器跟踪相结合,即第一级采用视日运动轨迹跟踪,初步跟踪太阳的运行轨迹,第二级采用传感器跟踪校正,并采用双轴式跟踪调整装置。系统还设计了时间显示模块,能够显示实时时间,同时也可以对时间进行实时调整。     

深度长时目标跟踪算法综述

在视觉目标跟踪领域,长时跟踪因存在更为复杂的遮挡、相似物干扰和目标消失等具有现实意义的挑战场景,而越来越被研究者所重视。传统长时跟踪算法存在精度低和效率低等问题,已经无法满足如视频监控和自动驾驶等领域对跟踪器性能的应用需求。目前,大量的研究工作通过引入深度神经网络快速推动了长时跟踪技术的发展。为了深入分析深度长时跟踪算法的现状与未来发展,通过对比长短时跟踪数据集及评价指标,初步界定了长时跟踪任务范畴,归纳了长时跟踪任务的需求和难点,并介绍了长时跟踪数据集及评价体系的发展。基于深度长时目标跟踪算法的设计框架,详细描述了框架各组成部分的设计思路。以长时跟踪策略为切入点深入分析了现有研究工作,归纳了不同模型的优缺点及特性。依据对现有研究工作的整理和总结,讨论了该领域面临的挑战,并对未来的发展方向进行了展望。     

Real time 3D face and facial feature tracking

Detecting and tracking human faces in video sequences is useful in a number of applications such as gesture recognition and human-machine interaction. In this paper, we show that online appearance models (holistic approaches) can be used for simultaneously tracking the head, the lips, the eyebrows, and the eyelids in monocular video sequences. Unlike previous approaches to eyelid tracking, we show that the online appearance models can be used for this purpose. Neither color information nor intensity edges are used by our proposed approach. More precisely, we show how the classical appearance-based trackers can be upgraded in order to deal with fast eyelid movements. The proposed eyelid tracking is made robust by avoiding eye feature extraction. Experiments on real videos show the usefulness of the proposed tracking schemes as well as their enhancement to our previous approach.

A 2D/3D model-based object tracking framework

This paper presents a robust framework for tracking complex objects in video sequences. Multiple hypothesis tracking (MHT) algorithm reported in (IEEE Trans. Pattern Anal. Mach. Intell. 18(2) (1996)) is modified to accommodate a high level representations (2D edge map, 3D models) of objects for tracking. The framework exploits the advantages of MHT algorithm which is capable of resolving data association/uncertainty and integrates it with object matching techniques to provide a robust behavior while tracking complex objects. To track objects in 2D, a 4D feature is used to represent edge/line segments and are tracked using MHT. In many practical applications 3D models provide more information about the object's pose (i.e., rotation information in the transformation space) which cannot be recovered using 2D edge information. Hence, a 3D model-based object tracking algorithm is also presented. A probabilistic Hausdorff image matching algorithm is incorporated into the framework in order to determine the geometric transformation that best maps the model features onto their corresponding ones in the image plane. 3D model of the object is used to constrain the tracker to operate in a consistent manner. Experimental results on real and synthetic image sequences are presented to demonstrate the efficacy of the proposed framework.     

Visual object tracking—classical and contemporary approaches

Visual object tracking (VOT) is an important subfield of computer vision. It has widespread application domains, and has been considered as an important part of surveillance and security system. VOA facilitates finding the position of target in image coordinates of video frames.While doing this, VOA also faces many challenges such as noise, clutter, occlusion, rapid change in object appearances, highly maneuvered (complex) object motion, illumination changes. In recent years, VOT has made significant progress due to availability of low-cost high-quality video cameras as well as fast computational resources, and many modern techniques have been proposed to handle the challenges faced by VOT. This article introduces the readers to 1) VOT and its applications in other domains, 2) different issues which arise in it, 3) various classical as well as contemporary approaches for object tracking, 4) evaluation methodologies for VOT, and 5) online resources, i.e., annotated datasets and source code available for various tracking techniques.