基于自适应的核相关滤波的目标跟踪算法

针对核相关滤波器(KCF)跟踪算法在目标发生尺度变化和严重遮挡的情况下跟踪失败的问题,提出了一种基于自适应的核相关滤波的目标跟踪算法。该算法运用了尺度估计策略,使跟踪框自适应,用多项式核函数来减少计算量,采用了FHog目标特征代替原来的Hog特征,获取更多的目标特征信息。实验采用OTB-2013评估基准的50组视频序列进行测试,并与其他31种跟踪算法进行对比,测试所提算法的有效性。实验结果表明:所提算法成功率为0.549,精确度为0.736,排名第一,与KCF算法相比,分别提高了3.8%和1.0%。该算法在目标发生尺度变化、严重遮挡等复杂情况下,均具有较强的稳健性和鲁棒性。     

基于重检测机制的核相关滤波跟踪算法

传统的核相关滤波跟踪(KCF)算法不能很好地处理目标快速移动和大面积遮挡,容易导致目标丢失.在KCF算法的基础上,提出了目标丢失检测、第一帧重检测、扩展区域重检测3种机制来解决以上问题.利用最大响应分数和平均峰值相关能量(APCE)来判别目标是否丢失;在目标即将丢失时,采用扩展区域重检测机制;在目标图像与第一帧目标图像...     

改进的核相关滤波跟踪算法

针对核相关滤波算法在目标跟踪过程中尺度特定和遮挡判断失败的问题,文中提出一种利用自适应特征融合的位置滤波器来判断目标是否被遮挡的方法。该方法检测到峰值旁瓣比异常时,停止模型自适应更新,启动在线重检测;并结合尺度金字塔中的尺度滤波器来确定目标尺寸,从而得出精准的目标位置。实验通过复杂背景下的10组运动视频来评估改进算法的性能。与基础核相关滤波算法相比,改进算法的平均中心位置误差降低了36.683 pixel;在像素阈值设为20 pixel时,平均距离精度提升了44.632%;在边界框重叠阈值设为0.5时,重叠精度提升了46.453%。     

基于改进的核相关滤波算法的红外目标跟踪

核相关滤波(KCF)算法在跟踪过程中由于目标受到遮挡或者目标本身的形变、大小变化等问题容易丢失目标.针对以上问题对原算法做出如下改进:一是加入检测模型响应值的方法作为判定目标是否丢失的依据,一旦判定目标丢失,模型就会暂停更新,增加采样窗口个数来扩大目标搜索范围,直至判定为重新定位目标;二是加入一种自适应的多尺度搜索策略...     

结合颜色模型的核相关滤波跟踪算法

为改善Staple目标跟踪算法的运行速度,在学习位置滤波器的过程中,对提取的图像特征进行PCA降维;在学习尺度滤波器的过程中,将提取的不同尺度样本个数由33减少至17,并且通过QR分解对尺度信息进行压缩。为保证尺度估计的准确性,在计算尺度响应时,使用插值法将尺度响应个数插值到33。实验结果显示,在准确率几乎不变的情况下,所提算法可将跟踪速度提升50%左右。     

基于特征融合和注意力机制的目标分割跟踪算法

在当前的目标跟踪领域,现有的基于分割的算法没有充分利用目标的长距离依赖信息和各个特征层的不同特性,前背景判别能力不强,对目标的多尺度估计不足。针对此问题,提出了自适应特征融合模块和混合域注意力模块,以提高网络对目标的多尺度估计能力和对目标的前背景辨别能力,并将其集成到当前基于视频分割的算法中,提出了一种新的目标跟踪算法,在各大公开数据集上的实验结果证明其达到了领先水平。     

基于候选区域检测的核相关目标跟踪算法

近年来,核相关滤波算法在目标跟踪领域应用广泛,表现出了非常优异的性能,但是核相关滤波类算法本质上属于模板匹配算法,并且缺乏跟踪失败恢复机制,在快速运动和快速形变情况下跟踪效果较差.针对以上问题,本文提出一种结合了核相关滤波跟踪算法和目标候选区域检测的跟踪算法,来改善核相关滤波跟踪算法的性能.算法主要设计了一种跟踪失败恢复机制,通过比较目标响应强度与经验阈值的大小,判断跟踪目标是否跟丢,当目标跟踪失败时,采用候选区域检测算法,在目标周围区域提取不同的检测图像块,确定目标在当前帧的最佳位置;然后,使用核相关滤波算法得到目标的精确位置,继续跟踪.此外,算法在跟踪模块中加入了颜色特征与梯度特征的自适应融合,进一步增强了算法的整体跟踪性能.实验结果证明,所提出算法在精确度和成功率上都表现出高效的性能,并且在快速运动和快速形变情况下跟踪性能要优于其余算法.     

一种改进的核相关滤波目标跟踪方法研究

针对目标跟踪领域视频图像序列经常出现遮挡、光照变化和快速移动等难点,提出了一种融合目标上下文信息和超像素级特征的核相关滤波跟踪方法,目标与其周围的信息存在相关性,利用中层视觉特征(超像素)构建目标的上下文滤波模型,在相关滤波框架下,实现对目标的快速训练和定位。实验结果表明,和其他相关滤波类算法相比,所提出的算法在处理以上跟踪难点时,精确度更高,且鲁棒性更强,同时能以较快的速度运行,满足实时性的要求。     

Kernelized correlation tracking based on point trajectories

Visual tracking is one of the most important directions in computer vision.However,many state-of-the-art algorithms cannot track the interested object reliably due to occlusion during tracking process,which leads to deficiency of object information.In order to solve occlusion problem,a kernelized correlation tracking method based on point trajectories was proposed.Through analyzing long-term motion cues of the local information,point trajectories were labeled by spectral clustering.These labeled points were used to differentiate the foreground and background objects and thus detect whether the target was occluded or drifts.If drifting and occlusion occur,re-detection was used to detect the re-entering of the target.Experimental results show that the proposed algorithm can handle occlusion and drifting problems effectively.     

基于核相关滤波器的颜色自适应目标跟踪算法

针对Staple算法中梯度直方图（HOG）特征和颜色直方图特征的融合无法自适应达到最优化的问题,本文提出了一种颜色自适应的核相关滤波目标跟踪改进算法,即Stronger-Staple算法（简称STR-Staple）。首先,本文用目标似然函数分别求出目标和背景所占比例的颜色直方图,并用巴氏系数实时测量目标与背景的颜色直方图的相似度,实现每一帧图像的跟踪监测;其次,提出一种自适应的融合系数,将相似度与融合系数相关联,对每一帧的特征匹配相应的权重,实现算法的最优融合。最后,本文算法在OTB-13和OTB-15两个数据集上与当前比较流行的5种跟踪算法进行比较。实验结果表明,该算法在光照变化、尺度变化、遮挡、变形、背景杂波等情况下均有较高的鲁棒性,且其跟踪精度、成功率在OTB-13数据集中分别为0.889、0.880。在OTB-15数据集中分别为0.741、0.644。均优于其它几种算法。      

基于相关滤波的目标重检测跟踪

近年来，相关滤波方法由于具备运算速度快，鲁棒性强的优势，在目标跟踪领域发展迅速。然而，面对复杂场景时，现有模型难以满足实际需求。针对背景感知相关滤波方法(BACF)在目标发生自身旋转、尺度变换、运动出视野等挑战下，相关滤波器最大响应值减弱，造成跟踪精度下降的问题，提出了一种基于相关滤波的目标重检测跟踪方法。在原有背景感知相关滤波方法的基础上，引入滤波器响应检测机制，当判定到相关滤波跟踪结果不可信时，利用粒子滤波采样策略生成大量粒子，感知目标状态，重新确定目标中心位置。在此基础上，利用自适应尺度估计机制重新计算目标尺度信息，从而实现对目标的重新跟踪。为了验证改进算法的有效性，实验选取了OTB2013、OTB2015、VOT2016共3个公开数据集进行测试，同时与相关滤波及深度学习方法进行对比，从视频属性、跟踪精确度、算法鲁棒性等角度展示所有算法的性能。实验结果表明:基于相关滤波的目标重检测跟踪方法在3个公开数据集中取得较好的实验结果，并在目标发生旋转，尺度变换及运动超出视野的情况下，有效提高了BACF的准确率和成功率。     

Support vector correlation filter with long-term tracking

Boosted by the promising advancement of the correlation filter-based tracker, we propose an algorithm called the SLT (support vector correlation filter with long-term tracking) that is based on the new SCF (support vector correlation filter) framework to handle long-term tracking. To perform long-term tracking, we propose using a detector to refine the position that includes occlusion and deformation and is out-of-view. We used a new judgment criterion called the max response to the average response rate (MAR) to activate the re-detection procedure and then exploit the linear support vector machine (SVM) classifier to obtain a positive refinement. Moreover, we do not update the SVM classifier every frame to reduce the number of computations and obtain better samples to improve the accuracy of the classifier. We use the online passive–aggressive learning algorithm for online learning and use the same MAR criterion to active it. Extensive experimental results on the OTB50 benchmark dataset show its superior performance in terms of accuracy and robustness.     

Video object tracking using adaptive Kalman filter

In this paper, a new video moving object tracking method is proposed. In initialization, a moving object selected by the user is segmented and the dominant color is extracted from the segmented target. In tracking step, a motion model is constructed to set the system model of adaptive Kalman filter firstly. Then, the dominant color of the moving object in HSI color space will be used as feature to detect the moving object in the consecutive video frames. The detected result is fed back as the measurement of adaptive Kalman filter and the estimate parameters of adaptive Kalman filter are adjusted by occlusion ratio adaptively. The proposed method has the robust ability to track the moving object in the consecutive frames under some kinds of real-world complex situations such as the moving object disappearing totally or partially due to occlusion by other ones, fast moving object, changing lighting, changing the direction and orientation of the moving object, and changing the velocity of moving object suddenly. The proposed method is an efficient video object tracking algorithm.     

基于粒子滤波的弹道目标跟踪

弹道目标再入段的运动受到空气阻力、重力等力的影响,具有明显的非线性特征.传统的卡尔曼滤波是线性、高斯问题的最优滤波器,但无法处理非线性的估计问题.扩展卡尔曼滤波利用泰勒级数展开把非线性方程线性化,是解决非线性估计问题的有效算法;而近些年来出现的粒子滤波以其解决非线性问题的卓越性能,得到了迅速发展.文章对弹道目标再入段的运动特征进行研究,建立了目标的状态空间模型,并应用扩展卡尔曼滤波和粒子滤波实现了对弹道目标的跟踪.通过比较仿真结果,证明粒子滤波比扩展卡尔曼滤波精度更高,对噪声的抑制能力更强,也更稳定.因而具有重大的研究意义.     

Probabilistic Kalman filter for moving object tracking

Kalman filter has been successfully applied to tracking moving objects in real-time situations. However, the filter cannot take into account the existing prior knowledge to improve its predictions. In the moving object tracking, the trajectories of multiple targets in the same environment could be available, which can be viewed as the prior knowledge for the tracking procedure. This paper presents the probabilistic Kalman filter (PKF) that is able to take into account the stored trajectories to improve tracking estimation. The PKF has an extra stage after two steps of the Kalman filter to refine the estimated position of the targets. The refinement is obtained by applying the Viterbi algorithm to a probabilistic graph, that is constructed based on the observed trajectories. The graph is built in the offline situation and could be adapted in the online tracking. The proposed tracker has higher accuracy compared to the standard Kalman filter and could handle widespread problems such as occlusion. Another significant achievement of the proposed tracker is to track an object with anomalous behaviors by drawing an inference based on the constructed probabilistic graph. The PKF was applied to several manually-built videos and several other video-bases containing severe occlusions, which demonstrates a significant performance in comparison with other state-of-the-art trackers.     

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.