基于相关滤波的红外目标跟踪抗遮挡处理

针对传统的相关滤波算法在红外目标跟踪过程中,目标被完全遮挡后跟踪失效的问题,提出一种结合了多尺度滤波跟踪器和基于深度学习检测器的目标实时跟踪抗遮挡算法.首先使用跟踪器跟踪目标,计算目标的峰值响应强度并比较峰值响应强度与经验阈值的大小以判断目标是否被遮挡或跟踪丢失.然后当目标被遮挡或跟踪丢失时,停止更新跟踪器,由于目标被...     

决策主导的多模式目标跟踪算法融合方法

针对视频目标跟踪过程中出现目标被遮挡情况导致跟踪器性能下降的问题,提出一种决策主导的多模式目标跟踪算法融合方法。该算法选取跟踪学习检测(TLD)算法和核相关滤波(KCF)算法作为集成方式的主体跟踪算法用于跟踪所选择视频目标。首先,使用直方图算法感知目标在运动过程中场景是否被遮挡的情况。然后,运用马尔可夫决策过程(MDP)利用上下文信息做出决策。最后,根据决策结果在目标被遮挡与否时选择TLD算法或KCF算法用于跟踪目标。仿真结果表明,提出的多模式目标跟踪算法集成方式较传统目标跟踪算法在实时性和鲁棒性综合性能上有更好的表现。     

基于时间一致性约束的核互相关器目标跟踪

针对相关滤波器跟踪算法在目标快速运动、遮挡和表观变化时易发生跟踪漂移或者丢失的问题,提出一种基于时间一致性和核互相关器的目标跟踪算法。该算法通过引入对图像噪声和杂波更具鲁棒性的核互相关向量,能够更精确地预测目标的仿射变化。同时,在学习过程中引入时间一致性约束,以解决因核相关器时间退化导致的跟踪漂移问题。最后,采用主灰度分量逆映射来提升跟踪器应对目标部分遮挡的能力。在公开的OTB100标准目标跟踪数据集中与提供的基准算法和其他性能更加先进的相关滤波算法进行对比,该算法平均跟踪速度为41f/s,相对fDSST和SAMF算法,其跟踪精度分别提升15.6%和6.4%,跟踪成功率分别提升33.3%和6.1%。实验结果表明,该算法在目标快速运动、遮挡或表观变化时仍能精确地跟踪目标。     

融合运动信息与表观信息的多目标跟踪算法

多目标跟踪是计算机视觉领域的重要研究方向,其在智能视频监控、人机交互、机器人导航、公共安全等领域有着重要的作用。目前目标跟踪算法仍面临诸多的挑战,例如遮挡、背景复杂、运动模糊等因素所造成的影响难以完全规避。文中基于一种简单的在线跟踪方法,提出一种融合多类信息的算法,有效地提升了跟踪器的性能。模型关注于帧与帧之间的目标检测与数据关联问题,依赖于不同帧之间目标运动与表观的相似性,当目标丢失及存在遮挡时,融合多源信息减少相关的不确定性。同时,该算法在真实环境中可实现实时跟踪的性能。实验评估结果表明,提出的跟踪器在公开数据集上具有良好的性能,可以显著减少目标丢失率以及身份交换率。     

改进卡尔曼滤波算法在伺服跟踪系统中的应用

针对图像跟踪器脱靶量数据噪声干扰较为严重的问题,提出了在伺服跟踪过程中采用卡尔曼滤波算法,并就遮挡和延时问题进行了相应的处理,在目标图像位置的精确预测以及目标速度的平滑方面获得了良好效果.实际应用中实现了高速度、高精度系统的平稳跟踪,达到了跟踪系统的技战术要求.     

微血管内皮细胞功能的研究进展

针对图像跟踪器脱靶量数据噪声干扰较为严重的问题,提出了在伺服跟踪过程中采用卡尔曼滤波算法,并就遮挡和延时问题进行了相应的处理,在目标图像位置的精确预测以及目标速度的平滑方面获得了良好效果.实际应用中实现了高速度、高精度系统的平稳跟踪,达到了跟踪系统的技战术要求.     

基于仿真视频的光电跟踪器测试

介绍了一种基于仿真视频的光电跟踪器测试方案。先制作、编辑仿真视频,并经PCI总线下载至视频输出卡,再统一发送至跟踪器。通过对比实时采集的跟踪器脱靶量信息、仿真图像的目标质心坐标、图像中心坐标,判定跟踪器的跟踪准确度和稳定度,达到对跟踪器测试目的。     

一种基于拓扑约束的多核跟踪算法

针对视觉跟踪中常见的目标部分遮挡和尺度变化问题,提出了一种基于拓扑约束的多核跟踪算法.首先,提取满足空间分布的位于目标与背景所在边界的Harris角点作为多核跟踪器的中心,然后,采用拓扑约束对多个跟踪结果进行优化,选取跟踪性能好的核跟踪器,构造仿射变换模型,进而得到最终跟踪结果和目标尺度变化信息.实验结果表明,本文算法能对目标进行准确跟踪,并能有效地处理目标的部分遮挡和尺度变化问题.     

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.     

基于局部二值模式的改进时空上下文跟踪算法

基于时空上下文(Spatial-Temporal Context,STC)的跟踪算法与大部分传统算法相比,在实时性方面具有明显的优势.通过实验发现,STC算法存在由变形和遮挡引起的跟踪精度下降问题.针对该问题,提出了一种改进方法,该方法在原STC算法的基础上引入局部二值模式(Local Binary Pattern,LBP)和遮挡检测机制,利用LBP特征来代替灰度特征,当跟踪器检测出目标发生遮挡时,停止分类器参数的更新.对于满足线性运动的目标,利用卡尔曼滤波器对其进行位置预测以解决目标发生遮挡后的定位问题.实验证明,所提出的改进算法能有效提升目标跟踪精度,针对遮挡情况下的目标也展现出较高的鲁棒性.     

Correlation,Kalman filter and adaptive fast mean shift based heuristic approach for robust visual tracking

Correlation tracker is computation intensive (if the search space or the template is large), has template drift problem, and may fail in case of fast maneuvering target, rapid changes in its appearance, occlusion suffered by it and clutter in the scene. Kalman filter can predict the target coordinates in the next frame, if the measurement vector is supplied to it by a correlation tracker. Thus, a relatively small search space can be determined where the probability of finding the target in the next frame is high. This way, the tracker can become fast and reject the clutter, which is outside the search space in the scene. However, if the tracker provides wrong measurement vector due to the clutter or the occlusion inside the search region, the efficacy of the filter is significantly deteriorated. Mean-shift tracker is fast and has shown good tracking results in the literature, but it may fail when the histograms of the target and the candidate region in the scene are similar (even when their appearance is different). In order to make the overall visual tracking framework robust to the mentioned problems, we propose to combine the three approaches heuristically, so that they may support each other for better tracking results. Furthermore, we present novel method for (1) appearance model updating which adapts the template according to rate of appearance change of target, (2) adaptive threshold for similarity measure which uses the variable threshold for each forthcoming image frame based on current frame peak similarity value, and (3) adaptive kernel size for fast mean-shift algorithm based on varying size of the target. Comparison with nine state-of-the-art tracking algorithms on eleven publically available standard dataset shows that the proposed algorithm outperforms the other algorithms in most of the cases.     

融合GMS检测和置信度判别的TLD目标跟踪

针对目标在遮挡、尺度变化等复杂场景下易产生模型漂移问题,基于跟踪学习检测(TLD)框架提出一种结合基于网格的运动统计(GMS)检测和置信度判别的长时目标跟踪算法。首先在跟踪模块中采用快速判别尺度空间的相关滤波器(fDSST)作为跟踪器,利用位置滤波器和尺度滤波器对上一帧目标进行位置与尺度的判别,并依据TLD算法中跟踪模块与检测模块的独立性,将跟踪模块结果输入检测模块中,采用平均峰值相关能量(APCE)对模板更新进行置信度判别。在检测模块中先引入GMS网格运动统计作为检测器,使具有快速旋转不变性特征的ORB(OrientedFASTandRotatedBRIEF)算法对上一帧目标进行特征匹配,再利用网格运动统计对匹配结果进行过滤,实现目标位置的粗定位,依据预测位置对目标检测区域进行适当的动态缩减,最后使用级联分类器对目标进行精准定位。结果表明,本文提出的跟踪方法在有效防止模型漂移的情况下,大大提高了算法的跟踪速度,同时对目标遮挡、尺度变化及旋转等挑战环境也具有较好的准确性和鲁棒性。     

一种基于交互式粒子滤波器的视频中多目标跟踪算法

在目标跟踪领域,常常通过建立先验模型,如路径一致性假设模型.对日标轨迹进行预测来处理跟踪过程中的遮挡问题.然而,当这种预测与目标的实际运动轨迹相差较大的时候就会发生跟踪失败.我们提出了一种交互式粒子滤波方法,通过判断不同目标样本观测之间的遮挡关系,自适应地选择不同外观模板进行相似性度量并更新粒子权值,成功地解决了跟踪过...     

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.     

模板自适应的Mean Shift红外目标跟踪

为了解决Mean Shift跟踪算法中目标模板只能从单一图像建立且很难更新问题,提出了一种结合改进的Mean Shift与增量式支持向量机的红外目标跟踪算法。首先,根据目标区域的灰度直方图对目标进行描述,然后采用标准Mean Shift搜索目标,结合子图图像矩特征进行二次搜索,再计算下一帧搜索的窗口大小,以解决目标尺寸明显变化时造成目标丢失的问题。同时,针对目标遮挡易导致跟踪失败的问题,引入机器学习理论,采用增量式支持向量机自适应更新模板,则目标跟踪问题转换为目标和背景的分类问题。实验结果表明:提出的改进算法在目标尺寸、姿态发生变化或出现部分遮挡时,能有效跟踪目标。     

Learning Scene Context for Multiple Object Tracking

We propose a framework for multitarget tracking with feedback that accounts for scene contextual information. We demonstrate the framework on two types of context-dependent events, namely target births (i.e., objects entering the scene or reappearing after occlusion) and spatially persistent clutter. The spatial distributions of birth and clutter events are incrementally learned based on mixtures of Gaussians. The corresponding models are used by a probability hypothesis density (PHD) filter that spatially modulates its strength based on the learned contextual information. Experimental results on a large video surveillance dataset using a standard evaluation protocol show that the feedback improves the tracking accuracy from 9% to 14% by reducing the number of false detections and false trajectories. This performance improvement is achieved without increasing the computational complexity of the tracker.      

位置-尺度异空间协调的多特征选择相关滤波目标跟踪算法

针对传统相关滤波跟踪算法在目标发生尺度变化和遮挡时容易导致跟踪失败的问题,本文提出位置-尺度异空间协调的多特征选择相关滤波目标跟踪算法。首先,提取目标区域的快速方向梯度直方图特征、颜色空间特征和灰度特征,特征间的不同组合方式构成特征池以加强滤波器的判别性能,将组合得到的特征分别进行相关滤波跟踪;其次,依据每种特征响应的鲁棒性得分,选择分数最高的响应图最大值预测目标位置;然后,转换坐标至对数极坐标中,使用相位相关滤波器进行目标尺度估计;最后,设计一种高置信度模型策略更新模板。在标准数据集TB-50和OTB-2015上的实验结果表明,本文提出的算法在目标发生尺度变化、遮挡、旋转、出视野和背景杂乱等情况下,仍具有较好的跟踪有效性。      

Learning discriminative update adaptive spatial-temporal regularized correlation filter for RGB-T tracking

The RGB-T trackers based on correlation filter framework have been extensively investigated for that they can track targets more accurately in most complex scenes. However, the performance of these trackers is limited when facing some specific challenging scenarios, such as occlusion and background clutter. For different tracking targets, most of these trackers utilize fixed regularization constraint to build the filter model, which is obviously unreasonable to effectively present the appearance changes and characteristics of a specific target. In addition, they adopt a simple model update mechanism based on linear interpolation, which can easily lead to model degradation in challenging scenarios, resulting in tracker drift. To solve the above problems, we propose a novel adaptive spatial-temporal regularized correlation filter model to learn an appropriate regularization for achieving robust tracking and a relative peak discriminative method for model updating to avoid the model degradation. Besides, to make better integrate the unique advantages of the two modes and adapt the changing appearance of the target, an adaptive weighting ensemble scheme and a multi-scale search mechanism are adopted, respectively. To optimize the proposed model, we designed an efficient ADMM algorithm, which greatly improved the efficiency. Extensive experiments have been carried out on two available datasets, RGBT234 and RGBT210, and the experimental results indicate that the tracker proposed by us performs favorably in both accuracy and robustness against the state-of-the-art RGB-T trackers.     

基于多特征自适应融合的抗遮挡目标跟踪算法

针对目前的目标跟踪算法在目标发生运动模糊或被遮挡等情况下跟踪效果较差,容易出现跟踪失败等情况,本文提出了一种多特征自适应融合的抗遮挡相关滤波跟踪算法。算法首先提取梯度方向直方图特征HOG和颜色直方图特征,以最大化跟踪质量为目标自适应融合两种特征的相关滤波响应;在跟踪的过程中根据响应图的质量存储高质量滤波模板,采用高质量模板和正常更新模板检测响应图的质量差值来检测目标的遮挡情况,当目标遮挡消失的时候,跟踪器的模板回溯到高质量模板来重新跟踪目标。根据在OTB100、UAV123的实验结果,本文算法相对于其他同类型的相关滤波在跟踪精度和成功率方面表现更好,在发生目标遮挡时仍能很好地跟踪。