基于改进活动轮廓的视频对象自动分割及跟踪算法

为了能够从视频序列图像中准确地提取出运动视频对象,提出了一种基于改进活动轮廓的视频对象自动分割及跟踪方法。该方法首先采用连续帧间差的4次统计量假设检验来确定视频对象的运动区域,并使用形态滤波消除残余噪声和空洞;然后根据3帧序列图像得到的前后运动区域的相与运算来有效地解决运动视频对象前后帧的遮挡问题,以获得视频对象模板,当提取出视频对象模板的边缘轮廓后,再用梯度向量流场作为外力的改进活动轮廓算法来获得视频对象的精确轮廓;最后以此视频对象的轮廓为基础进行运动补偿,以得到下一帧图像的初始曲线,再使用改进的活动轮廓算法对下一帧图像进行分割,即可实现视频对象的跟踪。该方法不仅能够消除差分图像中的显露背景,得到运动视频对象精确的轮廓,并且可进行多目标的分割与跟踪。     

基于运动估计的Kalman滤波视频对象跟踪

提出了一种利用Kalman滤波对运动目标的形心进行预测,从而实现视频对象跟踪的算法。首先进行视频对象分割,求出运动目标的形心。再利用视频序列中连续两帧的形心和运动矢量信息,用Kalman滤波对运动目标的形心在下一帧的位置进行预测,从而快速、有效地自动跟踪多个目标对象。实验结果表明,该算法对运动目标的出现和消失,以及非刚性物体的尺度变化和变形,具有较强的鲁棒性。     

Object tracking in image sequences using point features

This paper presents an object tracking technique based on the Bayesian multiple hypothesis tracking (MHT) approach. Two algorithms, both based on the MHT technique are combined to generate an object tracker. The first MHT algorithm is employed for contour segmentation. The segmentation of contours is based on an edge map. The segmented contours are then merged to form recognisable objects. The second MHT algorithm is used in the temporal tracking of a selected object from the initial frame. An object is represented by key feature points that are extracted from it. The key points (mostly corner points) are detected using information obtained from the edge map. These key points are then tracked through the sequence. To confirm the correctness of the tracked key points, the location of the key points on the trajectory are verified against the segmented object identified in each frame. If an acceptable number of key-points lie on or near the contour of the object in a particular frame (n-th frame), we conclude that the selected object has been tracked (identified) successfully in frame n.     

用于立体视频会议系统的立体对象分割与跟踪算法

基于对象的立体视频编码压缩技术能在立体视频会议系统中得到很好的应用，从立体视频信号中正确分割出立体视频对象是基于对象的立体视频编码压缩的一个前提条件，基于立体视频会议图像序列的时空特性和左右通道间的视差特性，提出了一种立体视频对象分割与跟踪算法，首先利用空域分割和运动检测相结合的方法，提取左通道中的运动物体；然后，提出一种左右通道间基于边缘轮廓的二级视差匹配算法，并根据已分割的左通道运动对象提取右通道的视频运动对象；最后利用对象边界轮廓的跟踪方法对后续图像中的运动对象进行快速跟踪，实验结果说明文中算法能够应用于立体视频会议图像序列的立体对象分割与跟踪。     

融合图像显著性与特征点匹配的形变目标跟踪

目的 针对目标在跟踪过程中出现剧烈形变,特别是剧烈尺度变化的而导致跟踪失败情况,提出融合图像显著性与特征点匹配的目标跟踪算法。方法 首先利用改进的BRISK（binary robust invariant scalable keypoints）特征点检测算法,对视频序列中的初始帧提取特征点,确定跟踪算法中的目标模板和目标模板特征点集合;接着对当前帧进行特征点检测,并与目标模板特征点集合利用FLANN（fast approximate nearest neighbor search library）方法进行匹配得到匹配特征点子集;然后融合匹配特征点和光流特征点确定可靠特征点集;再后基于可靠特征点集和目标模板特征点集计算单应性变换矩阵粗确定目标跟踪框,继而基于LC（local contrast）图像显著性精确定目标跟踪框;最后融合图像显著性和可靠特征点自适应确定目标跟踪框。当连续三帧目标发生剧烈形变时,更新目标模板和目标模板特征点集。结果 为了验证算法性能,在OTB2013数据集中挑选出具有形变特性的8个视频序列,共2214帧图像作为实验数据集。在重合度实验中,本文算法能够达到0.567 1的平均重合度,优于当前先进的跟踪算法;在重合度成功率实验中,本文算法也比当前先进的跟踪算法具有更好的跟踪效果。最后利用Vega Prime仿真了无人机快速抵近飞行下目标出现剧烈形变的航拍视频序列,序列中目标的最大形变量超过14,帧间最大形变量达到1.72,实验表明本文算法在该视频序列上具有更好的跟踪效果。本文算法具有较好的实时性,平均帧率48.6帧/s。结论 本文算法能够实时准确的跟踪剧烈形变的目标,特别是剧烈尺度变化的目标。     

一种基于梯度方向信息的运动目标检测算法

运动目标检测是智能视觉监控系统的基本内容。在对现有算法分析的基础上提出了一种基于梯度方向信息的运动目标检测算法。首先利用方向信息提取视频图像序列中每一帧的边缘梯度图,然后通过改进传统帧差算法,采用uint8数据格式处理含有时间关系的两帧图像以此确定运动目标粗略边界,经运动目标连通域识别,最后结合梯度方向信息准确提取运动目标的完整轮廓。实验结果表明,该算法克服了传统帧差算法不能准确定位目标的缺点,在室内外复杂背景下均能准确地提取完整的目标轮廓。     

Rapidly constructed appearance models for tracking in augmented reality applications

This paper presents a method for rapidly generating crude, appearance-based edge models consisting of a set of planes. The appearance of each plane is modeled using a set of keyframes containing a list of edgels. These models are generated from a short video sequence with a few annotated frames indicating the location of the object of interest. The data requires 3–5?min to collect using a handheld device instrumented with a camera. The models produced are used with an existing edge tracking algorithm modified to select the appropriate edge keyframe and detect occlusion. A framestore is also created containing several views of the object represented as sets of point features. The framestore is used to provide an initial, rough pose estimate for initializing contour tracking. The presented system is used to create an augmented reality application to guide a user through a machine tool setup and a printer maintenance task. The models are shown to be an accurate representation of the object. Additionally, the performance of various aspects of the model making and tracking algorithms are evaluated.     

Fast object tracking using adaptive block matching

We propose a fast object tracking algorithm that predicts the object contour using motion vector information. The segmentation step common in region-based tracking methods is avoided, except for the initialization of the object. Tracking is achieved by predicting the object boundary using block motion vectors followed by updating the contour using occlusions/disocclusion detection. An adaptive block-based approach has been used for estimating motion between frames. An efficient modulation scheme is used to control the gap between frames used for motion estimation. The algorithm for detecting disocclusion proceeds in two steps. First, uncovered regions are estimated from the displaced frame difference. These uncovered regions are classified into actual disocclusions and false alarms by observing the motion characteristics of uncovered regions. Occlusion and disocclusion are considered as dual events and this relationship is explained in detail. The algorithm for detecting occlusion is developed by modifying the disocclusion detection algorithm in accordance with the duality principle. The overall tracking algorithm is computationally superior to existing region-based methods for object tracking. The immediate applications of the proposed tracking algorithm are video compression using MPEG-4 and content retrieval based on standards like H.264. Preliminary simulation results demonstrate the performance of the proposed algorithm.     

基于二进小波变换的主动轮廓模型

针对传统的主动轮廓模型对噪声敏感的缺陷,在推广梯度矢量流的基础上,提出了基于二进小波变换的改进外力场的计算方法.通过综合利用图像小波分解的高频信息,迭代计算获得其外力场,使蛇在图像的多尺度空间中搜索目标轮廓.针对蛇模型对初始轮廓的依赖性问题,给出了在一维最大熵阈值分割后的图像上获取初始轮廓点的方法,减少了人工的干预,加快了蛇的收敛速度和效果.实验结果表明该模型能有效地排除噪声的干扰,搜索凹陷轮廓,而且对脆弱轮廓有很好的逼近能力.     

一种视频序列中的运动对象自动分割算法

提出了一种自动、准确的运动对象分割算法。首先通过直方图拟合获得准确的背景噪声方差,克服了以往只能依据经验设定背景噪声方差的缺点,并使用显著性测试技术有效地对帧差图进行二值化,确定出运动区域。然后进行形态学和对称差分处理消除噪声及显露背景,获得初始运动对象。但由于分割结果不够精确,再使用梯度向量流场作为外力的改进活动轮廓算法得到运动对象精确轮廓。实验结果表明,该方法能够得到运动对象精确的轮廓,并且具有调整参数少,抗干扰能力强,可并行处理等优点。     

基于条件对抗生成孪生网络的目标跟踪

为解决被跟踪目标在快速剧烈运动时,因运动模糊和低分辨率使模型发生漂移,导致跟踪器跟踪效果变差甚至跟踪失败的问题,对全卷积孪生网络跟踪算法(SiamFC)进行改进,提出一种基于条件对抗生成孪生网络的目标跟踪算法(CGANSiamFC).首先,在SiamFC框架的基础上嵌入条件对抗生成网络模块,对输入的低分辨率模糊视频帧去...     

梯度矢量流Snake模型临界点剖析

作为经典Snake模型的一个变体,梯度矢量流(gradient vector flow,简称GVF)Snake在扩大Snake轮廓的捕捉范围和深度凹陷区域的收敛上具有卓越的性能.但GVF Snake在初始化时存在一个临界点问题:在目标内部的临界点必须在初始Snake轮廓的内部;在目标外部的临界点必须在Snake轮廓的外部.否则,Snake轮廓将不能收敛到正确的结果.对GVF Snake的临界点问题进行探讨,详细分析了临界点的影响因素,指出GVF场只有在合适的条件下才是有效的;证明了相关文献中从粘性流体力学     

基于时空上下文信息增强的目标跟踪算法

充分利用视频中的时空上下文信息能明显提高目标跟踪性能,但目前大多数基于深度学习的目标跟踪算法仅利用当前帧的特征信息来定位目标,没有利用同一目标在视频前后帧的时空上下文特征信息,导致跟踪目标易受到邻近相似目标的干扰,从而在跟踪定位时会引入一个潜在的累计误差。为了保留时空上下文信息,在SiamMask算法的基础上引入一个短期记忆存储池来存储历史帧特征;同时,提出了外观显著性增强模块（ASBM）,一方面增强跟踪目标的显著性特征,另一方面抑制周围相似目标对目标的干扰。基于此,提出一种基于时空上下文信息增强的目标跟踪算法。在VOT2016、VOT2018、DAVIS-2016和DAVIS-2017等四个数据集上进行实验与分析,结果表明所提出的算法相较于SiamMask算法在VOT2016上的准确率和平均重叠率（EAO）分别提升了4个百分点和2个百分点;在VOT2018上的准确率、鲁棒性和EAO分别提升了3.7个百分点、2.8个百分点和1个百分点;在DAVIS-2016上的区域相似度、轮廓精度指标中的下降率均分别降低了0.2个百分点;在DAVIS-2017上的区域相似度、轮廓精度指标中的下降率分别降低了1.3和0.9个百分点。     

Contour graph based human tracking and action sequence recognition

This paper introduces a new framework for human contour tracking and action sequence recognition. Given a gallery of labeled human contour sequences, we define each contour as a “word” and encode all of them into a contour dictionary. This dictionary will be used to translate the video. To this end, a contour graph is constructed by connecting all the neighboring contours. Then, the motion in a video is viewed as an instance of random walks on this graph. As a result, we can avoid explicitly parameterizing the contour curves and modeling the dynamical system for contour updating. In such a work setting, there are only a few state variables to be estimated when using sequence Monte Carlo (SMC) approach to realize the random walks. In addition, the walks on the graph also perform sequence comparisons implicitly with those in the predefined gallery, from which statistics about class label is evaluated for action recognition. Experiments on diving tracking and recognition illustrate the validity of our method.     

Efficient edge-based object tracking

Object tracking has been widely applied to video surveillance, robot localization and human-computer interaction. In this paper, an edge-based tracking algorithm is proposed. We extract the feature points by efficiently utilizing the image edges in the object region. Then the parameter vector of the object's motion model is estimated based on minimizing the sum-of-squared differences between the reference feature points in the reference frame and the observed feature points in the tracking sequence frame. The experiments show that the edge-based tracking algorithm proposed by us can track object efficiently under uniform and varying illumination conditions.     

High-speed target tracking by fuzzy hostility-induced segmentation of optical flow field

A time efficient technique for real-time tracking of high-speed objects in a video sequence is presented in this article. The technique is primarily based on the segmentation of the optical flow field computed between the successive image frames of a video sequence, followed by the tracking of a detected point of interest (POI) within the segmented flow field. In the initial phase of the technique, the optical flow field between the first two successive image frames acquired from a video sequence, is computed. A fuzzy hostility index indicative of the degree of coherence of the moving objects in the image frames, is used to segment the optical flow field. This yields different coherent regions of interest (ROIs) in the segmented flow field. A POI is then detected in the different ROIs obtained. Tracking of the moving object is then carried out by computing the flow fields between predefined ROIs in the neighborhood of the detected POI in the subsequent image frames.Since the selected ROIs are smaller than the image frames, a fair amount of reduction in the time required for the computation of the optical flow field is achieved, thereby facilitating real-time operation. An application of the proposed technique is demonstrated on three video sequences of high-speed flying fighter aircrafts.     

基于GVF和压力Snake模型的哑铃型目标提取

针对传统活动模型初始化曲线严格的位置选择问题和梯度矢量流场模型存在的哑铃型目标"临界点"问题,提出了梯度矢量流场构造出气球压力的活动轮廓改进模型.利用了梯度矢量流场决定形变点的压力方向,在该构造压力与图像外力及轮廓曲线内力的共同作用下模型完成目标提取.结合实例对改进模型和算法进行了试验分析,结果表明了模型的可行性和算法的有效性.     

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。     

一种新的FAST-Snake目标跟踪方法

提出一种新的FAST-Snake目标跟踪方法,利用改进的FAST角点特征匹配来估计目标轮廓在帧间的全局仿射变换,将投影轮廓点作为Snake模型的初始化轮廓.为提高跟踪实时性,在Snake能量模型中定义了先验约束能,并用限定搜索方向的贪婪算法（Greedy algorithm）实现局部轮廓优化.实验包括三维目标数据库及真实场景视频,验证了提出方法的均方误差（Means quare error,MSE）及收敛速度评估均优于对比算法,并具备对复杂运动及局部遮挡的适应能力.