A hybrid tracking method for scaled and oriented objects in crowded scenes

Traditional kernel based means shift assumes constancy of the object scale and orientation during the course of tracking and uses a symmetric/asymmetric kernel, such as a circle or an ellipse for target representation. In a tracking scenario, it is not uncommon to observe objects with complex shapes whose scale and orientation constantly change due to the camera and object motions. In this paper, we propose a multi object tracking method which tracks the complete object regions, adapts to changing scale and orientation, and assigns consistent labels to each object throughout real world video sequences. Our approach has five major components: (1) dynamic background subtraction, (2) level sets, (3) mean shift convergence, (4) object identification, and (5) occlusion handling. The experimental results show that the proposed method is superior to the traditional mean shift tracking in the following aspects: (1) it provides consistent multi objects tracking instead of single object throughout the video, (2) it is not affected by the scale and orientation changes of the tracked objects, (3) its computational complexity is much less than traditional mean shift due to using level set method instead of probability density.     

Online adaptive radial basis function networks for robust object tracking

Visual tracking has been a challenging problem in computer vision over the decades. The applications of visual tracking are far-reaching, ranging from surveillance and monitoring to smart rooms. In this paper, we present a novel online adaptive object tracker based on fast learning radial basis function (RBF) networks. Pixel based color features are used for developing the target/object model. Here, two separate RBF networks are used, one of which is trained to maximize the classification accuracy of object pixels, while the other is trained for non-object pixels. The target is modeled using the posterior probability of object and non-object classes. Object localization is achieved by iteratively seeking the mode of the posterior probability of the pixels in each of the subsequent frames. An adaptive learning procedure is presented to update the object model in order to tackle object appearance and illumination changes. The superior performance of the proposed tracker is illustrated with many complex video sequences, as compared against the popular color-based mean-shift tracker. The proposed tracker is suitable for real-time object tracking due to its low computational complexity.     

基于SIFT 特征的目标多自由度mean-shift 跟踪算法

传统的mean-shift跟踪算法不能跟踪目标的旋转、缩放运动,且常常因此造成定位不准.鉴于此,将尺度不变特征变换（SIFT）特征检测融入到mean-shift跟踪过程,提出SIFT特征点的尺度变化与目标的尺度变化成正比,特征点主方向变化与目标旋转角度一致,给出了基于SIFT特征的自适应目标尺度、方向计算方法,且利用带方向、可变带宽的椭圆核改进传统的mean-shift跟踪方法.实验表明,该算法能够较好地跟踪目标的旋转、缩放运动,定位也更准确.     

Approximate Bayesian methods for kernel-based object tracking

A framework for real-time tracking of complex non-rigid objects is presented. The object shape is approximated by an ellipse and its appearance by histogram based features derived from local image properties. An efficient search procedure is used to find the image region with a histogram most similar to the histogram of the tracked object. The procedure is a natural extension of the mean-shift procedure with Gaussian kernel which allows handling the scale and orientation changes of the object. The presented procedure is integrated into a set of Bayesian filtering schemes. We compare the regular and mixture Kalman filter and other sequential importance sampling (particle filtering) techniques.     

Contour-based object tracking with occlusion handling in video acquired using mobile cameras

We propose a tracking method which tracks the complete object regions, adapts to changing visual features, and handles occlusions. Tracking is achieved by evolving the contour from frame to frame by minimizing some energy functional evaluated in the contour vicinity defined by a band. Our approach has two major components related to the visual features and the object shape. Visual features (color, texture) are modeled by semiparametric models and are fused using independent opinion polling. Shape priors consist of shape level sets and are used to recover the missing object regions during occlusion. We demonstrate the performance of our method in real sequences with and without object occlusions.     

一种抗遮挡的自适应尺度目标跟踪算法

在处理尺度变化和目标遮挡方面,利用相关滤波器的不同特征进行目标跟踪仍然存在问题。提出了一种基于随机蕨丛检测器的多尺度核相关滤波器算法。该算法将跟踪任务分解为目标尺度估计和位移估计,同时将CN颜色特征和HOG特征进行响应融合,进一步提高了整体跟踪性能。此外,文中训练了一个在线随机蕨分类器,在目标丢失后其能重新获取目标。与KCF,DSST,TLD,MIL,CT共5种算法相比,所提算法不仅能够准确地估计目标状态,而且可以有效处理目标的遮挡问题。     

Shape based appearance model for kernel tracking

This paper investigates kernel based tracking using shape information. A kernel based tracker typically models an object with a primitive geometric shape, and then estimates the object state by fitting the kernel such that the appearance model is optimized. Most of the appearance models in kernel based tracking utilize the textural information within the kernel, although a few of them also make use of the gradient information along the kernel boundary. Interestingly, shape information of a general form has never been fully exploited in kernel tracking, despite the fact that shape has been widely used in silhouette tracking at the cost of intensive computation. In this paper, we propose an original way to incorporate shape knowledge into the appearance model of kernel based trackers while preserving their computational advantage versus silhouette based trackers. Experimental results demonstrate that kernel tracking is strongly improved by exploiting the proposed shape cue through comparisons to both kernel and silhouette trackers.     

Mean-Shift跟踪算法中核函数窗宽的自动选取

传统核窗宽固定的Mean-Shift跟踪算法不能很好地对逐渐增大尺寸的目标进行有效的跟踪.在分析同一目标在不同尺度下核直方图基于Bhattacharyya系数相似性的基础上,发现并证明了在核窗宽固定的条件下,目标在其窗宽范围内进行缩放、平移运动并不影响Mean-Shift跟踪算法空间定位的准确性.在此基础上,提出了一种基于后向跟踪、形心配准的核窗宽自动选取算法.对尺度渐大的车辆进行的跟踪实验验证了该算法的有效性.     

A motion observable representation using color correlogram and its applications to tracking

This paper presents a special form of color correlogram as representation for object tracking and carries out a motion observability analysis to obtain the optimal correlogram in a kernel based tracking framework. Compared with the color histogram, where the position information of each pixel is ignored, a simplified color correlogram (SCC) representation encodes the spatial information explicitly and enables an estimation algorithm to recover the object orientation. In this paper, based on the SCC representation, the mean shift algorithm is developed in a translation–rotation joint domain to track the positions and orientations of objects. The ability of the SCC in detecting and estimating object motion is analyzed and a principled way to obtain the optimal SCC as object representation is proposed to ensure reliable tracking. Extensive experimental results demonstrate SCC as a viable object representation for tracking.     

基于PF的窗口自适应Mean-Shift跟踪算法

在视频目标跟踪过程中,Mean-Shift算法存在着核函数带宽固定不变的缺陷,对尺度大小发生变化的目标无法进行有效跟踪。提出一种多尺度理论与粒子滤波器（PF）相结合的改进算法。通过粒子滤波器对多尺度理论统计得到的跟踪窗信息量进行预测修正,据此计算核窗宽大小变化的比例系数,实现跟踪算法的窗口自适应能力。实验结果表明,改进的跟踪算法对尺寸逐渐减小和逐渐增大的目标均能自动选择合适的跟踪窗口大小。     

Adaptive visual tracking using the prioritized Q-learning algorithm: MDP-based parameter learning approach

This paper introduces an adaptive visual tracking method that combines the adaptive appearance model and the optimization capability of the Markov decision process. Most tracking algorithms are limited due to variations in object appearance from changes in illumination, viewing angle, object scale, and object shape. This paper is motivated by the fact that tracking performance degradation is caused not only by changes in object appearance but also by the inflexible controls of tracker parameters. To the best of our knowledge, optimization of tracker parameters has not been thoroughly investigated, even though it critically influences tracking performance. The challenge is to equip an adaptive tracking algorithm with an optimization capability for a more flexible and robust appearance model. In this paper, the Markov decision process, which has been applied successfully in many dynamic systems, is employed to optimize an adaptive appearance model-based tracking algorithm. The adaptive visual tracking is formulated as a Markov decision process based dynamic parameter optimization problem with uncertain and incomplete information. The high computation requirements of the Markov decision process formulation are solved by the proposed prioritized Q-learning approach. We carried out extensive experiments using realistic video sets, and achieved very encouraging and competitive results.     

A novel algebraic method for kernel-based object tracking

In the present paper, a new tracking method based on kernel tracking is proposed. The proposed method employs a novel algebraic algorithm to get the kernel movement. In contrast to the mean-shift method which uses a weighted kernel to reduce the effect of the background, the algebraic algorithm of the proposed method allows dividing the candidate area into two parts in order to identify the object and background regions. To detect the object and background regions, we propose measuring the similarity of weighted histogram for each part. The experiments show the superiority of the proposed method for the removal of the background. The effect of noise and background clutter is reduced by segmentation of the object which produces the narrow histogram. In conclusion, the ability of the proposed method for tracking in crowded and cluttered scenes is demonstrated.     

Online learning and fusion of orientation appearance models for robust rigid object tracking

We introduce a robust framework for learning and fusing of orientation appearance models based on both texture and depth information for rigid object tracking. Our framework fuses data obtained from a standard visual camera and dense depth maps obtained by low-cost consumer depth cameras such as the Kinect. To combine these two completely different modalities, we propose to use features that do not depend on the data representation: angles. More specifically, our framework combines image gradient orientations as extracted from intensity images with the directions of surface normals computed from dense depth fields. We propose to capture the correlations between the obtained orientation appearance models using a fusion approach motivated by the original Active Appearance Models (AAMs). To incorporate these features in a learning framework, we use a robust kernel based on the Euler representation of angles which does not require off-line training, and can be efficiently implemented online. The robustness of learning from orientation appearance models is presented both theoretically and experimentally in this work. This kernel enables us to cope with gross measurement errors, missing data as well as other typical problems such as illumination changes and occlusions. By combining the proposed models with a particle filter, the proposed framework was used for performing 2D plus 3D rigid object tracking, achieving robust performance in very difficult tracking scenarios including extreme pose variations.     

基于自适应组合核的鲁棒视频目标跟踪算法

为了解决核化相关滤波器（KCF）在复杂场景下鲁棒性差的问题,提出了基于自适应组合核（SACK）的目标跟踪算法。跟踪任务分为位置跟踪和尺度跟踪两个独立部分。首先,以线性核和高斯核的自适应组合作为核跟踪滤波器,构造了SACK权重的风险目标函数。该函数根据核的响应值自适应调整线性核和高斯核权重,不仅考虑了不同核响应输出的经验风险泛函最小,而且考虑了极大响应值的风险泛函,同时具有局部核和全局核的优点。然后,根据该滤波器的输出响应得到目标精确位置,设计了基于目标极大响应值的自适应更新率,针对位置跟踪滤波器进行自适应更新。最后,利用尺度跟踪器对目标尺度进行估计。实验结果表明,所提算法的成功率和距离精度在OTB-50数据库表现最优,比KCF算法分别高6.8个百分点和4.1个百分点,比双向尺度估计跟踪（BSET）算法分别高2个百分点和3.2个百分点。该算法对形变和遮挡等复杂场景具有很强的适应能力。     

判别特征回归的鲁棒目标跟踪

目的 近年来,目标跟踪领域取得了很大进步,但是由于尺度变化,运动,形状畸变或者遮挡等造成的外观变化,仍然是目标跟踪中的一大挑战,因而有效的图像表达方法是提高目标跟踪鲁棒性的一个关键因素。方法 从中层视觉角度出发,首先对训练图像进行超像素分割,将得到特征向量集以及对应的置信值作为输入值,通过特征回归的方法建立目标跟踪中的判别外观模型,将跟踪图像的特征向量输入该模型,得到候选区域的置信值,从而高效地分离前景和背景,确定目标区域。结果 在公开数据集上进行跟踪实验。本文算法能较好地处理目标尺度变化、姿态变化、光照变化、形状畸变、遮挡等外观变化;和主流跟踪算法进行对比,本文算法在跟踪误差方面表现出色,在carScale、subway、tiger1视频中能取得最好结果,平均误差为12像素,3像素和21像素;和同类型的方法相比,本文算法在算法效率上表现出色,所有视频的跟踪效率均高于同类型算法,在carScale视频中的效率,是同类算法效率的32倍。结论 实验结果表明,本文目标跟踪算法具有高效性和鲁棒性,适用于目标发生外观变化时的目标跟踪问题。目前跟踪中只用了单一特征,未来考虑融合多特征来提升算法鲁棒性和准确度。     

一种基于SVM的核相关跟踪算法

基于相关滤波器的跟踪方法在准确度和鲁棒性上取得了突出优势,但仍需要提高整体的跟踪性能.针对传统单目标的核相关滤波器跟踪算法在目标尺度变化和产生遮挡的跟踪中存在的问题,提出了一种结合支持向量机(SVM)检测器的多尺度相关滤波器算法.通过在核矩阵中引入尺度因子来提高相关滤波器处理尺度变换的性能,训练了一个在线SVM检测器,当目标发生遮挡时,能够重新获取目标,同时自适应调整模型学习率.通过与其他5种优秀跟踪算法进行实验比较,结果表明:方法能够广泛应用于目标跟踪领域,对目标进行准确地估计并有效处理目标的遮挡问题.     

鉴别式多核手跟踪

快速运动、背景混杂及手的特殊性为传统的核目标跟踪技术提出了挑战.文中提出一种鉴别式颜色子空间选择和多核校正机制的核跟踪方法.首先基于前景背景建模,通过鉴别函数选取最优鉴别式颜色子空间集,再基于训练的肤色模型保留高置信子空间表征目标.其次,为适应尺度变化和快速运动,基于多核快速估计相似度表面以校正初始核位置,迭代定位目标.实验表明,融合二者的跟踪器在有效性和速度上都实现了满意的性能,满足实时性需要.     

密度分布特征及其在二值图像检索中的应用

图像的形状是描述图像的重要视觉和语义特征,可通过图像中像素点的区域分布表现出来。为了对二值图像进行有效检索,提出了一种基于区域的形状特征——密度分布特征,用来进行二值图像检索。该方法在经过形心定位和子图像区域划分后,可得到2个M维特征向量,其中第1个表示各个子图像区域的目标像素的相对密度,第2个表示各个子图像区域的目标像素在极坐标方向上的相对密度的一阶数值差分。在进行相似性度量时,首先采用Gaussian模型对用这2个特征向量计算得到的距离分别进行归一化处理;然后综合两个特征向量的距离计算总的相似度。实验结果表明,密度分布特征不仅能够有效地刻画二值图像的形状,具有非常好的平移、尺度和旋转不变性,而且检索结果优于Hu不变矩。     

基于极坐标区间运算的2D形状匹配

形状匹配是遥感图像目标识别、字符识别、手形识别和步态识别等任务中的关键步骤之一.针对刚体识别任务中形状匹配易受方向、尺度和位置等仿射变化量影响的情况,提出了一种新的基于极坐标区间运算的2D形状匹配算法.该算法首先以形状区域的中心点为极点,区域的最长轴方向为极轴,对形状区域进行归一化的极坐标变换;然后定义了同一角度对应的区域内点区间之间的运算;最后定义了两个区域归一化极坐标变换结果在区间运算下的相似度函数,用以表征两个区域之间的匹配度.从可见光遥感图像中提取的实物图像实验结果证明,该方法能够有效归类相似形状,并能区分各类不同的形状.     

基于贝叶斯模型与最佳伙伴相似度量的目标跟踪

提出了一种基于产生式与判别式联合模型的视觉目标跟踪算法。首先介绍了一种基于全局颜色特征直方图特征的贝叶斯分类器,检测出若干最有可能属于目标的候选区域,然后利用最佳伙伴相似性度量（Best-Buddies Similarity）得到候选区域与目标模板的相似度,结合概率值与相似度值估计出最优的目标状态。通过划分目标-背景区域模型、目标-干扰区域模型,对可能产生干扰的区域提前进行抑制,降低了长期跟踪可能产生的漂移问题的风险,同时引入了自适应尺度估计机制和在线模型更新策略,以获得更为精准的跟踪结果。在37组具有挑战性的图像序列上与7种优秀的算法对比实验表明,所提出的算法能够有效应对光照变化、遮挡、旋转与尺度变化等多种问题。