基于三维直方图的改进Camshift目标跟踪算法

经典的连续自适应均值漂移算法Camshift通过HSV空间的色调Hue分量建立一维直方图,在有光照变化及有相似颜色目标或背景的干扰下,跟踪效果不好。提出一种融合HSV空间中色调、饱和度以及反应物体形状信息的边缘梯度的三维直方图特征,并基于背景模型自适应调整特征直方图三种分量的权重值,提高了算法的跟踪准确度。通过与传统Camshift跟踪实验比较,提出的改进算法在光照变化及相似颜色目标/背景干扰下具有更好的鲁棒性,同样也满足跟踪系统的实时性要求。     

使用自适应背景约束改进的颜色属性跟踪算法

颜色属性跟踪算法使用的核脊回归分类器对于背景信息的利用过少,导致算法在目标快速运动、局部遮挡和背景相似物干扰等情形下容易发生漂移。针对此问题,首先,通过计算响应矩阵峰值旁瓣比确定干扰峰所在位置;然后,在核脊回归分类器上引入对应的背景约束项来加强分类器对背景信息的利用;最后,使用构建尺度空间滤波器的方法实现对多尺度变化的支持。在Visual Tracker Benchmark数据集上的对比实验结果表明,该方法在不过多损失算法速度的前提下,能够有较高的跟踪精度。     

一种对光照变化鲁棒的均值漂移跟踪方法

颜色作为一个有效的视觉特征,被广泛的用于基于表面模型的跟踪中。但在跟踪过程中,由于光照、视角及摄像机参数等的变化,往往会造成目标颜色的改变,使得跟踪不稳定。该文提出了一种新的基于局部背景动态修正模糊颜色直方图的均值漂移跟踪方法,在颜色-空间域运用核密度估计建立目标的模糊颜色直方图模型,利用目标的局部背景动态修正目标模型,克服了基于传统颜色直方图建立目标模型时对于光照变化较为敏感的缺点。实验验证了该文算法可以平滑相似性表面,减小局部极值点对跟踪的影响,在光照剧烈变化的情况下能够实时鲁棒地跟踪目标。     

一种自适应基于纹理和颜色特征的目标跟踪算法

针对运动目标进行跟踪,常采用粒子滤波跟踪算法。为了减少相似背景像素点及光照变化对机动目标跟踪的干扰,采用了基于目标纹理特征和颜色特征融合的自适应粒子滤波算法,通过采用了不同的状态转移模型和观测模型,根据不同的跟踪环境进行自适应选择,并给出了相应的实验结果。实验结果表明,该算法在跟踪的性能和鲁棒性方面有所改进。     

车载视频下改进的核相关滤波跟踪算法

针对相关滤波跟踪算法在车载视频下由于环境复杂及目标尺度变化等情况下容易跟踪失败的问题,该文提出一种基于背景信息的尺度自适应相关滤波跟踪算法。首先利用背景感知相关滤波跟踪器融合方向梯度直方图特征预测目标下一帧位置,然后根据预测位置选取图像块进行检测,最后结合动态尺度比例金字塔模型对目标进行尺度估计。实验选取了KITTI数据库中23段车载视频和标注国内的4段车载视频进行测试,实验结果表明,该算法能有效降低车载环境的复杂背景、目标尺度变化等因素干扰,整体性能优于KCF, DSST, SAMF, SATPLE等主流相关滤波算法,对车载环境下复杂背景和尺度变化的目标跟踪具有鲁棒性。     

基于全局背景与特征降维的视觉跟踪算法

相关滤波算法容易受到形变、运动模糊、相似背景等因素的干扰,导致跟踪任务失败。为了克服以上问题,该文提出一种基于全局背景与特征降维的视觉跟踪算法。该算法首先提取紧邻目标的图像区域作为负样本供分类器学习,以抑制相似背景的干扰;然后提出一种基于主成分分析的更新策略,构建降维矩阵压缩HOG特征的维度,在更新分类器的同时减少其冗余度;最后加入颜色特征表征运动目标,并根据特征对系统状态的响应强度进行自适应融合。在标准数据集上将该文提出的算法与Staple, KCF等其他算法进行了仿真对比,结果表明该文算法具有更强的鲁棒性,在形变因素的影响下,所提出的算法与Staple和KCF算法相比距离精度分别提升8.3%和13.1%。     

一种改进的Camshift视频目标跟踪算法

鉴于连续自适应均值漂移(Camshift)算法在光照变化,相似背景颜色干扰及目标遮挡时鲁棒性不高,易造成跟踪错误等问题,提出了一种联合多特征和最大类间方差法的视频运动目标跟踪算法。该算法将色度直方图、梯度方向直方图和LBP纹理特征进行巧妙的融合,构建了一种高效的联合直方图目标外观特征模型,并在Camshift算法中嵌入最大类间方差法,增强目标和背景的区分度。不同场景的视频跟踪结果表明,改进算法有效克服了传统Camshift算法应对光照变化、颜色干扰和目标遮挡的缺点,与同类算法相比,具有更高的准确度和鲁棒性。     

一种红外和可见光双通道视频目标跟踪方法

室外场景下由于场景背景条件变化容易导致视频目标跟踪稳定性差。该文提出一种利用红外和可见光传感器的双通道视频目标跟踪方法。该算法利用可见光图像的目标颜色特征和红外图像的目标轮廓特征,结合均值漂移算法与水平集曲线演化实现目标定位,并给出了目标尺度和模板更新方法;对多目标跟踪的互相遮挡问题,通过判断目标合并与分离实现遮挡时多个目标的定位。实验结果表明,该文方法能够有效处理光照变化、阴影、遮挡等情况,实现目标的稳定跟踪。     

基于ICA分布模型的粒子滤波跟踪算法

为了在复杂背景、部分遮挡和光照变化等因素干扰的情况下鲁棒地跟踪视频序列中感兴趣的运动目标,提出了一种改进的粒子滤波跟踪算法。该算法针对颜色信息在目标表述中存在的不足,首先对观测模型进行改进,提出了一种基于ICA特征分布的目标模型,将基于核函数的目标特征描述转换到ICA特征空间,由于光照变化引起灰度变化经ICA后仍是同一分量,因此能有效的适应光照变化,不仅考虑并充分利用了空间信息。有效的解决了光照变化及背景颜色相近造成的目标丢失现象,提高了目标跟踪算法的鲁棒性。同时,考虑到粒子的退化现象,将均值平移算法嵌入到粒子滤波的跟踪框架中,待各粒子经过系统传播后,利用均值平移算法使粒子向其领域局部极大值处移动,使得粒子集中在测量模型的局部区域内,只需少量的粒子就覆盖了尽可能的目标分布,很好地克服了粒子滤波器的退化现象并有效缩短了计算时间,提高了目标跟踪算法的准确性和系统的实时性。实验表明,该算法不仅能在复杂背景下准确的跟踪目标,而且在光线变化和部分遮挡情况下也能保证不丢失目标。      

基于多特征融合的均值迁移粒子滤波跟踪算法

仅用单一的颜色特征进行跟踪是大多数跟踪算法鲁棒性不高的主要原因。针对此问题,该文提出一种多特征融合跟踪算法。该算法利用颜色和纹理特征表示目标,通过均值迁移和粒子滤波算法进行特征融合,有效地避免了单一颜色特征在光照变化和背景相似情况下的不稳定问题。将两种常用的融合策略结合,减轻了粒子的退化现象,提高了算法效率。实验结果表明该算法提取的目标特征具有较强的鲁棒性,能实现复杂场景下的目标跟踪。     

Mean LBP and modified fuzzy C-means weighted hybrid feature for illumination invariant mean-shift tracking

Object tracking is a critical task in surveillance and activity analysis. Two main issues for tracking are appearance (illumination) and structural (size of a target) variations of the object. We propose a method which is robust and addresses these issues by incorporating features that are less variant to these changes. The proposed features are mean local binary pattern (mLBP), an illumination invariant texture feature, and modified fuzzy c-means (MFCM) weighted color histogram to handle both illumination and scale changes. These features are combined to form a hybrid mean-shift (MS) vector and used in the MS vector framework for target tracking. Experimental results using standard benchmark videos show that the proposed scheme can lead to better localization and robust tracking in challenging illumination scenarios, when compared to several existing tracking algorithms.     

Invariant‐Feature Based Object Tracking Using Discrete Dynamic Swarm Optimization

With the remarkable growth in rich media in recent years, people are increasingly exposed to visual information from the environment. Visual information continues to play a vital role in rich media because people's real interests lie in dynamic information. This paper proposes a novel discrete dynamic swarm optimization (DDSO) algorithm for video object tracking using invariant features. The proposed approach is designed to track objects more robustly than other traditional algorithms in terms of illumination changes, background noise, and occlusions. DDSO is integrated with a matching procedure to eliminate inappropriate feature points geographically. The proposed novel fitness function can aid in excluding the influence of some noisy mismatched feature points. The test results showed that our approach can overcome changes in illumination, background noise, and occlusions more effectively than other traditional methods, including color‐tracking and invariant feature‐tracking methods.     

基于HSV颜色空间的自适应性运动目标检测

格拉斯曼尼(Grassmannian)算法是一种可以由高度不完整信息追踪子空间的在线学习算法,它在视频运动目标跟踪时具有鲁棒性和低复杂度等优点,可以应用在视频前景与背景的实时分离的情况.针对格拉斯曼尼算法在前景分离中,面对室内全局光线突变会产生大量噪声的问题,提出了一种优化的预处理方法.通过HSV色彩空间变换对视频进行阴影检测,根据阈值判断光线变化情况并自适应调整前景内容,最终实现在光照变化情况下的运动目标检测,并有效去除了原格拉斯曼尼算法在光线突变会产生的大量噪声,提高了对光照变化的鲁棒性.     

An efficient real-time target tracking algorithm using adaptive feature fusion

Visual-based target tracking is easily influenced by multiple factors, such as background clutter, targets’ fast-moving, illumination variation, object shape change, occlusion, etc. These factors influence the tracking accuracy of a target tracking task. To address this issue, an efficient real-time target tracking method based on a low-dimension adaptive feature fusion is proposed to allow us the simultaneous implementation of the high-accuracy and real-time target tracking. First, the adaptive fusion of a histogram of oriented gradient (HOG) feature and color feature is utilized to improve the tracking accuracy. Second, a convolution dimension reduction method applies to the fusion between the HOG feature and color feature to reduce the over-fitting caused by their high-dimension fusions. Third, an average correlation energy estimation method is used to extract the relative confidence adaptive coefficients to ensure tracking accuracy. We experimentally confirm the proposed method on an OTB100 data set. Compared with nine popular target tracking algorithms, the proposed algorithm gains the highest tracking accuracy and success tracking rate. Compared with the traditional Sum of Template and Pixel-wise LEarners (STAPLE) algorithm, the proposed algorithm can obtain a higher success rate and accuracy, improving by 2.3% and 1.9%, respectively. The experimental results also demonstrate that the proposed algorithm can reach the real-time target tracking with 50+fps. The proposed method paves a more promising way for real-time target tracking tasks under a complex environment, such as appearance deformation, illumination change, motion blur, background, similarity, scale change, and occlusion.     

基于粒子滤波与多特征融合的视频目标跟踪

提出了一种基于粒子滤波和多特征融合的视频目标跟踪方法.以粒子滤波为跟踪框架,根据颜色跟踪中存在的问题提出将颜色与目标运动信息融合,利用融合后的信息确定粒子的权值.利用重采样策略缓解退化现象对粒子滤波的影响.针对2段不同的视频进行了不同算法的仿真与性能的比较,实验结果表明,本文方法在计算量增加不多的情况下大大改善了跟踪的性能与鲁棒性,尤其当目标与背景颜色相近时仍然能够准确地对目标进行跟踪.     

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.     

基于区域统计及YUV色度特性的阴影消除

基于阴影区域统计及YUV色度特性,提出一种鲁棒的运动阴影检测方法。首先,在待检测像素点邻域划分小区域,利用小区域内光照条件基本相同、阴影近似线性地降低背景亮度的属性,估计出背景的亮度;接着,与混合高斯模型(GMM)得到的背景相减,统计差值图像的加权方差,作为判定阴影的特征;最后,依据邻域光照变化率不变性与YUV色度差异进一步消除误判阴影点。经多个室内外的经验数据集实验表明,该方法阴影检测率高,前景空洞少,且有较强的抗干扰能力。