子空间模型下的仿射不变目标跟踪

针对目标跟踪过程中目标可能出现的快速变化和严重遮挡等问题,提出了一种基于新的子空间表示的目标跟踪算法。采用距离不变量对尺度不变特征变换(SIFT)特征点匹配对进行提纯。用提纯后的特征点匹配对,通过线性拟合得到仿射变化参数。在粒子滤波的理论框架下,采用快速的迭代算法,建立目标的主分量(PCA)子空间表示,结合计算得到的仿射变化参数,构造有效的目标观测模型完成跟踪。同时,采用在线学习的方法对SIFT特征点和PCA子空间进行定时更新。大量实验表明,提出的算法能快速有效地完成对姿态和形状剧烈变化的目标的精确跟踪。     

基于SIFT 匹配的多视点立体图像零视差调整

针对目标跟踪过程中目标可能出现的快速变化和严重遮挡等问题,提出了一种基于新的子空间表示的目标跟踪算法。采用距离不变量对尺度不变特征变换(SIFT)特征点匹配对进行提纯。用提纯后的特征点匹配对,通过线性拟合得到仿射变化参数。在粒子滤波的理论框架下,采用快速的迭代算法,建立目标的主分量(PCA)子空间表示,结合计算得到的仿射变化参数,构造有效的目标观测模型完成跟踪。同时,采用在线学习的方法对SIFT特征点和PCA子空间进行定时更新。大量实验表明,提出的算法能快速有效地完成对姿态和形状剧烈变化的目标的精确跟踪。     

基于粒子滤波与样本加权的压缩跟踪算法

该文针对压缩跟踪算法无法适应目标尺度的变化以及没有考虑样本权重的问题,提出一种基于粒子滤波与样本加权的压缩跟踪算法。首先,对压缩特征进行改进,提取归一化矩形特征用于构建目标表观模型。然后,引入样本加权的思想,根据正样本与目标之间距离的不同赋予正样本不同的权重,提高分类器的分类精度。最后,在粒子滤波的框架下融合尺度不变压缩特征进行动态状态估计,在粒子预测阶段利用2阶自回归模型对粒子状态进行估计与预测,借助观测模型对粒子状态进行更新,并且对粒子进行重采样以防止粒子退化。实验结果表明,相比于原始压缩跟踪算法,改进算法能够更好地跟踪目标尺度的变化,提高跟踪的稳定性和准确性。     

结合时空上下文信息的相关滤波目标跟踪方法

针对繁杂环境下目标跟踪稳定性差且易受到遮挡发生漂移的问题,提出一种结合时空上下文信息的相关滤波目标跟踪方法。该算法首先从目标和背景区域提取方向梯度直方图特征和颜色直方图特征,加权融合两种特征的相关滤波响应,建立相关滤波跟踪模型;然后利用目标的背景梯度直方图特征,基于贝叶斯框架通过最大化似然函数得到时空上下文辅助模型;最后自适应融合两种模型响应,得到目标估计位置并采用尺度估计方法解决目标尺度变化问题。在OTB-2013公开标准测试集上与基于相关滤波的运动目标跟踪方法进行了实验对比。结果表明,该算法的平均距离精度值和平均重叠精度值都优于其他算法,能够有效缓解跟踪目标由于遮挡、尺度变化、光照等因素造成的跟踪漂移状况的发生。     

改进高斯混合粒子滤波被动目标跟踪算法

针对被动传感器跟踪系统非线性较强问题,提出了一种基于改进高斯混合粒子滤波的被动传感器目标跟踪算法。该算法基于Sigma点卡曼滤波和粒子滤波的特点,用有限的高斯混合模型来近似后验状态密度、系统噪声和观测噪声的分布。然后结合遗传算法和EM算法来实现模型的降阶,克服了EM算法假定混合成分数为已知、迭代的结果需要依赖初始值、可能收敛到局部最大点或可能收敛到参数空间的边界的缺点,从而改善粒子枯竭的问题。仿真实验结果表明在被动传感器跟踪领域,与传统粒子滤波、基于EM的高斯混合粒子滤波和基于贪心EM的高斯混合粒子滤波相比,该算法在保持高精度估计能力的同时,具有较强的鲁棒性,是解决非线性系统状态估计问题的一种有效方法。     

基于代价参考粒子滤波的机动目标跟踪算法

代价参考粒子滤波算法通过动态优化自定义代价函数和风险函数来衡量状态滤波估计的质量,选取最优的状态估计.与粒子滤波算法相比其优点是不需要任何先验概率知识的假定和重采样过程可实现并行处理.将代价参考粒子滤波与当前统计模型的优点相结合,提出一种新的当前统计模型自适应跟踪算法,用于非线性非高斯系统的机动目标跟踪.Monte Carlo仿真表明,该算法跟踪精度优于标准的交互多模型算法和当前统计模型自适应跟踪算法,实时性好于当前统计模型高斯粒子滤波算法.     

一种基于边缘特征的地面军事目标相关跟踪方法

针对传统的相关跟踪方法搜索量大和易受外界环境因素的影响,考虑到军事环境下地面目标的特殊性,提出了一种基于边缘特征的相关跟踪方法。在粒子滤波跟踪框架下,通过利用canny算子获取边缘方向直方图构造目标模板,自适应更新模板,同时,采用卡尔曼滤波进行遮挡情况下的状态估计,进行有效的跟踪。实验结果表明,该算法状态估计性能好,鲁棒性较高。     

基于强跟踪滤波器的多传感器非线性动态系统状态与参数联合估计

本文将强跟踪滤波理论与多传感器数据融合技术相结合,提出基于强跟踪滤波器的多传感器状态与参数联合估计新算法;对拥有相同采样率的分布式多传感器单模型非线性动态系统,应用强跟踪滤波器,得到目标状态基于全局信息融合估计结果,并利用计算机仿真结果对算法的有效性进行了验证;这些工作初步解决了Kalman滤波中由于模型的不确定性而造成估计误差值偏大情况下的状态融合估计问题,从而丰富和发展了多源信息融合理论.     

基于代价参考粒子滤波器组的检测前跟踪算法

基于粒子滤波的检测前跟踪方法是检测和估计非线性调频信号的有效方法之一.但此类方法运算量大,难以并行执行.此外,由于粒子滤波算法收敛较慢,基于粒子滤波的检测前跟踪方法的检测和状态估计能力有待提高.针对上述问题,该文首先提出一种代价参考粒子滤波器组.该滤波器组收敛快速,具有完全的并行结构,可快速准确地估计非线性调频信号的瞬时频率.其次,提出基于代价参考滤波器组的检测前跟踪算法,可在给定虚警率下,在各个时刻检测目标和估计目标状态.两类非线性调频信号检测和估计的仿真结果表明,基于代价参考粒子滤波器组的检测前跟踪算法的检测性能、估计性能和运行速率均优于类似的方法,如基于粒子滤波的检测前跟踪方法,基于Rutten粒子滤波的检测前跟踪方法等.     

基于粒子滤波的目标跟踪算法

粒子滤波是一种可在非线性和非高斯情形下进行状态估计的有效方法.文中介绍了一种基于粒子滤波来实现目标跟踪的具体方法,给出了粒子滤波的基本原理以及在该理论框架下进行目标跟踪的具体算法和实现步骤.     

基于概率图模型目标建模的视觉跟踪算法

提出了一种视觉跟踪任务中基于局部特征和概率图模型的目标建模方法,将目标表示为一组具有仿射不变性的区域特征,并通过概率图模型描述特征之间的空间约束关系。在目标跟踪过程中,首先在空域上利用信任传播算法,推断概率图模型中各个特征的状态,然后根据推断的结果设计改进的重要性采样函数,采用粒子滤波算法在时间域上对目标进行跟踪。为了适应目标在运动中的变化,模型根据特征的稳定程度自适应地进行更新。实验结果表明,该方法具有较强的鲁棒性,能够有效实现复杂场景下的目标跟踪。     

Robust Visual Tracking with SIFT Features and Fragments Based on Particle Swarm Optimization

We propose a novel approach for visual tracking based on a particle swarm optimization (PSO) framework using SIFT feature points correspondence and multiple fragments in a candidate target region to cope with the problems of partial occlusions, illumination changes, and large motion changes of the tracked target. Firstly, optimal search in the successive frame tracking process is performed by the PSO algorithm, which guides all particles towards the global optima state based on a fitness function. Then, the SIFT feature information is integrated into the iterative results of PSO to acquire a more accurate tracking state. Secondly, we present an effective appearance model updating criterion, which evaluates which fragments in appearance model need updating at each frame. However, the fragments with occluded parts or low quality measure values are not updated. The method for updating appearance model is introduced to improve the tracking performance. Compared with state-of-the-art algorithms, the proposed method can still stably track the target during the course of long-term partial occlusions using superior fragments of tracked target. The experiment results demonstrate the effectiveness of our algorithm in complex environments where the target object undergoes partial occlusions and large changes in pose and illumination.     

Robust appearance-guided particle filter for object tracking with occlusion analysis

A major challenge for most tracking algorithms is how to address the changes of object appearance during tracking, incurred by large illumination, scale, pose variations and occlusions. Without any adaptability to these variations, the tracker may fail. In contrast, if adapts too fast, the appearance model is likely to absorb some improper part of the background or occluding objects. In this paper, we explore a tracking algorithm based on the robust appearance model which can account for slow or rapid changes of object appearance. Specifically, each pixel in appearance model is represented using mixture Gaussian models whose parameters are on-line learned by sequential kernel density approximation. The appearance model is then embedded into particle filter framework. In addition, an occlusion handling scheme is invoked to explicitly indicate outlier pixels and deal with occlusion events, thus avoiding the appearance model to be contaminated by undesirable outlier ‘thing’. Extensive experiments demonstrate that our appearance-based tracking algorithm can successfully track the object in the presence of dramatic appearance changes, cluttered background and even severe occlusions.     

Collaborative object tracking model with local sparse representation

There existed many visual tracking methods that are based on sparse representation model, most of them were either generative or discriminative, which made object tracking more difficult when objects have undergone large pose change, illumination variation or partial occlusion. To address this issue, in this paper we propose a collaborative object tracking model with local sparse representation. The key idea of our method is to develop a local sparse representation-based discriminative model (SRDM) and a local sparse representation-based generative model (SRGM). In the SRDM module, the appearance of a target is modeled by local sparse codes that can be formed as training data for a linear classifier to discriminate the target from the background. In the SRGM module, the appearance of the target is represented by sparse coding histogram and a sparse coding-based similarity measure is applied to compute the distance between histograms of a target candidate and the target template. Finally, a collaborative similarity measure is proposed for measuring the difference of the two models, and then the corresponding likelihood of the target candidates is input into a particle filter framework to estimate the target state sequentially over time in visual tracking. Experiments on some publicly available benchmarks of video sequences showed that our proposed tracker is robust and effective.     

基于主分量寻踪的鲁棒视觉跟踪

传统子空间跟踪易受到模型漂移的影响而导致跟踪失败.针对此问题,本文提出一种基于主分量寻踪的鲁棒视觉跟踪方法.该方法以多个模板张成的子空间作为目标表观模型,利用主分量寻踪求解候选目标的误差分量,在粒子滤波框架下利用候选目标的误差分量估计最优状态参数.为了适应目标表观变化并克服模型漂移,本文提出一种模板更新方法.当跟踪结果与目标模板相似时,该方法利用跟踪结果更新目标模板,否则利用跟踪结果的低秩分量更新目标模板.在多个具有挑战性的图像序列上的实验结果表明:与现有跟踪方法相比,文中的跟踪方法具有较优的跟踪性能.     

Efficient object tracking by incremental self-tuning particle filtering on the affine group

We propose an incremental self-tuning particle filtering (ISPF) framework for visual tracking on the affine group, which can find the optimal state in a chainlike way with a very small number of particles. Unlike traditional particle filtering, which only relies on random sampling for state optimization, ISPF incrementally draws particles and utilizes an online-learned pose estimator (PE) to iteratively tune them to their neighboring best states according to some feedback appearance-similarity scores. Sampling is terminated if the maximum similarity of all tuned particles satisfies a target-patch similarity distribution modeled online or if the permitted maximum number of particles is reached. With the help of the learned PE and some appearance-similarity feedback scores, particles in ISPF become "smart" and can automatically move toward the correct directions; thus, sparse sampling is possible. The optimal state can be efficiently found in a step-by-step way in which some particles serve as bridge nodes to help others to reach the optimal state. In addition to the single-target scenario, the "smart" particle idea is also extended into a multitarget tracking problem. Experimental results demonstrate that our ISPF can achieve great robustness and very high accuracy with only a very small number of particles.     

复杂场景下基于多特征融合的视频跟踪

为了解决常见视频跟踪方法在复杂场景中难以有效跟踪运动物体的难题,研究了在粒子滤波框架下基于多特征融合的判别式视频跟踪算法.首先分析了特征提取和跟踪算法的鲁棒性和准确性的关系,指出融合多种特征能有效地提升算法在复杂场景中的跟踪效果,然后选择提取HSV颜色特征和HOG特征描述目标表观,并在线训练逻辑斯特回归分类器构造判别式目标表观模型.在公开的复杂场景视频进行测试,比较了使用单一特征和多种特征的实验效果,并且将所提算法和经典跟踪算法进行了比较,实验结果表明融合多种特征的视频跟踪更具鲁棒性和准确性.     

基于仿射变换模型的图像跟踪系统的实现

文中设计研制了一种新型的基于仿射变换模型的实时图像跟踪系统。本跟踪系统已经通过实践检验,能够稳定的、准确的、快速的跟踪目标。并且系统有很大的升级潜力,除了能够满足仿射变换跟踪的要求之外,还能适用于其他的一些算法,构成鲁棒性更强的图像跟踪系统。实践证明该跟踪系统性能优于经典的相关跟踪系统。     

Visual tracking in high-dimensional state space by appearance-guided particle filtering

In this paper, we propose a new approach, appearance-guided particle filtering (AGPF), for high degree-of-freedom visual tracking from an image sequence. This method adopts some known attractors in the state space and integrates both appearance and motion-transition information for visual tracking. A probability propagation model based on these two types of information is derived from a Bayesian formulation, and a particle filtering framework is developed to realize it. Experimental results demonstrate that the proposed method is effective for high degree-of-freedom visual tracking problems, such as articulated hand tracking and lip-contour tracking.     

L2范数正则化鲁棒编码视觉跟踪

针对基于稀疏表示的视觉跟踪计算效率低和易于产生模型漂移的不足,该文提出一种基于L2范数正则化鲁棒编码的视觉跟踪方法。该方法利用L2范数正则化鲁棒编码求解候选目标的编码系数,以粒子滤波为框架,利用候选目标的加权重建误差建立似然模型跟踪目标。为了适应目标的变化并克服模型漂移问题,利用L2范数正则化鲁棒编码估计当前目标的加权矩阵用于遮挡检测,根据遮挡检测结果实现模型更新。对提出的跟踪方法进行实验的结果表明:与现有跟踪方法相比,该方法具有较优的跟踪性能。