Learning Structure Models with Context Information for Visual Tracking

Tracking objects that undergo abrupt appearance changes and heavy occlusions is a challenging problem which conventional tracking methods can barely handle.To address the problem, we propose an online structure learning algorithm that contains three layers: an object is represented by a mixture of online structure models (OSMs) which are learnt from block-based online random forest classifiers (BORFs).BORFs are able to handle occlusion problems since they model local appearances of the target.To further improve the tracking accuracy and reliability, the algorithm utilizes mixture relational models (MRMs) as multi-mode context information to integrate BORFs into OSMs.Furthermore, the mixture construction of OSMs can avoid over-fitting effectively and is more flexible to describe targets.Fusing BORFs with MRMs, OSMs capture the discriminative parts of the target, which guarantees the reliability and robustness of our tracker.In addition, OSMs incorporate with block occlusion reasoning to update our BORFs and MRMs, which can deal with appearance changes and drifting problems effectively.Experiments on challenging videos show that the proposed tracker performs better than several state-of-the-art algorithms.     

Tracking using Numerous Anchor Points

In this paper, an online adaptive model-free tracker is proposed to track single objects in video sequences to deal with real-world tracking challenges like low-resolution, object deformation, occlusion and motion blur. The novelty lies in the construction of a strong appearance model that captures features from the initialized bounding box and then are assembled into anchor point features. These features memorize the global pattern of the object and have an internal star graph-like structure. These features are unique and flexible and help tracking generic and deformable objects with no limitation on specific objects. In addition, the relevance of each feature is evaluated online using short-term consistency and long-term consistency. These parameters are adapted to retain consistent features that vote for the object location and that deal with outliers for long-term tracking scenarios. Additionally, voting in a Gaussian manner helps in tackling inherent noise of the tracking system and in accurate object localization. Furthermore, the proposed tracker uses pairwise distance measure to cope with scale variations and combines pixel-level binary features and global weighted color features for model update. Finally, experimental results on a visual tracking benchmark dataset are presented to demonstrate the effectiveness and competitiveness of the proposed tracker.     

一种自适应尺度与学习速率调整的背景感知相关滤波跟踪算法

针对跟踪过程中遮挡因素以及目标尺度变化因素导致的目标跟踪漂移问题,文中提出了一种自适应尺度与学习速率调整的背景感知相关滤波跟踪算法。该算法首先通过背景感知相关滤波器获得目标的初步位置信息;其次在背景感知相关滤波器的基础框架下训练尺度相关滤波器,以有效估计目标尺度变化,从而准确调整搜索区域的大小;然后根据响应图波动情况进行遮挡判定,利用平均峰值能量指标与最大响应值判定目标遮挡情况,自适应调整模型学习速率大小;最后,设计相应的模型更新策略,来提高模型性能。在OTB100 Benchmark数据集上进行测试,实验结果表明,该算法与背景感知相关滤波器相比,其成功率提高了6.2%,精度提高了10.1%,因此该算法能有效地处理遮挡、尺度变化等问题,提高了跟踪模型的成功率与准确率,同时具有实时的跟踪速度。     

在线多示例学习目标跟踪方法研究

多示例学习是不同于传统机器学习的一种新的学习模式,近年来被应用于图像检索、文本分类等领域。提出一种基于在线学习的多示例学习算法,将其应用于目标跟踪。该算法通过构造一个在线学习的多示例分类器作为检测器,无需制作大量的样本进行离线的训练,只需在第一帧手动选中目标,便可以自动生成正样本和负样本,并在随后的帧序列中,根据跟踪到的目标自动更新分类器,在跟踪器丢失目标或者目标从场景中消失后,它能够重新检测到目标并更新跟踪器,从而有效地支持了跟踪器跟踪目标。实验证明该方法在背景复杂,光线变化,摄像机抖动等复杂条件下,可以很好地跟踪到目标,且对遮挡具有较好的鲁棒性。     

A parallel and robust object tracking approach synthesizing adaptive Bayesian learning and improved incremental subspace learning

This paper presents a novel tracking algorithm which integrates two complementary trackers. Firstly, an improved Bayesian tracker(B-tracker) with adaptive learning rate is presented. The classification score of B-tracker reflects tracking reliability, and a low score usually results from large appearance change. Therefore, if the score is low, we decrease the learning rate to update the classifier fast so that B-tracker can adapt to the variation and vice versa. In this way, B-tracker is more suitable than its traditional version to solve appearance change problem. Secondly, we present an improved incremental subspace learning method tracker(Stracker). We propose to calculate projected coordinates using maximum posterior probability, which results in a more accurate reconstruction error than traditional subspace learning tracker. Instead of updating at every time, we present a stopstrategy to deal with occlusion problem. Finally, we present an integrated framework(BAST), in which the pair of trackers run in parallel and return two candidate target states separately. For each candidate state, we define a tracking reliability metrics to measure whether the candidate state is reliable or not, and the reliable candidate state will be chosen as the target state at the end of each frame. Experimental results on challenging sequences show that the proposed approach is very robust and effective in comparison to the state-of-the-art trackers.     

Visual tracking via weakly supervised learning from multiple imperfect oracles

Notwithstanding many years of progress, visual tracking is still a difficult but important problem. Since most top-performing tracking methods have their strengths and weaknesses and are suited for handling only a certain type of variation, one of the next challenges is to integrate all these methods and address the problem of long-term persistent tracking in ever-changing environments. Towards this goal, we consider visual tracking in a novel weakly supervised learning scenario where (possibly noisy) labels but no ground truth are provided by multiple imperfect oracles (i.e., different trackers). These trackers naturally have intrinsic diversity due to their different design strategies, and we propose a probabilistic method to simultaneously infer the most likely object position by considering the outputs of all trackers, and estimate the accuracy of each tracker. An online evaluation strategy of trackers and a heuristic training data selection scheme are adopted to make the inference more effective and efficient. Consequently, the proposed method can avoid the pitfalls of purely single tracking methods and get reliably labeled samples to incrementally update each tracker (if it is an appearance-adaptive tracker) to capture the appearance changes. Extensive experiments on challenging video sequences demonstrate the robustness and effectiveness of the proposed method.     

遮挡判别下的多尺度相关滤波跟踪算法

目的 复杂环境下,运动目标在跟踪过程中受尺度变换以及遮挡因素的影响,跟踪准确率较低。针对这一问题,提出一种遮挡判别下的多尺度相关滤波跟踪方法。方法 首先选取第1帧图像的前景区域,训练目标的位置、尺度滤波器和GMS（grid-based motion statistics）检测器。然后,通过位置滤波器估计目标位置,尺度滤波器计算目标尺度,得到初选目标区域。最后,利用相关滤波响应情况对初选目标区域进行评估,通过相关滤波响应值的峰值和峰值波动情况判断是否满足遮挡和更新条件。若遮挡,启动检测器检测目标位置,检测到目标位置后,更新目标模型;若更新,则更新位置、尺度滤波器和GMS检测器,完成跟踪。结果 本文使用多尺度相关滤波方法作为算法的基本框架,对尺度变化目标跟踪具有较好的适应性。同时,利用目标模型更新机制和GMS检测器检索目标,有效地解决了遮挡情况下的目标丢失问题。在公开数据集上的测试结果表明,本文算法平均中心误差为5.58,平均跟踪准确率为94.2%,跟踪速度平均可达27.5 帧/s,与当前先进的跟踪算法相比,本文算法兼顾了跟踪速度和准确率,表现出更好的跟踪效果。结论 本文提出一种新的遮挡判别下的多尺度相关滤波跟踪算法。实验结果表明,本文算法在不同的尺度变换及遮挡条件下能够快速准确跟踪目标,具有较好的跟踪准确率和鲁棒性。     

具有尺度和旋转适应性的长时间目标跟踪

目标发生尺度和旋转变化会给长时间目标跟踪带来很大的挑战,针对该问题,本文提出了具有尺度和旋转适应性的鲁棒目标跟踪算法.首先针对跟踪过程中目标存在的尺度变化和旋转运动,提出一种基于傅里叶-梅林变换和核相关滤波的目标尺度和旋转参数估计方法.该方法能够实现连续空间的目标尺度和旋转参数估计,采用核相关滤波提高了估计的鲁棒性和准确性.然后针对长时间目标跟踪过程中,有时不可避免地会出现跟踪失败的情况（例如由于长时间半遮挡或全遮挡等）,提出一种基于直方图和方差加权的目标搜索方法.当目标丢失时,通过提出的搜索方法能够快速从图像中确定目标可能存在的区域,使得跟踪算法具有从失败中恢复的能力.本文还训练了两个核相关滤波器用于估计跟踪结果的置信度和目标平移,通过专门的核相关滤波器能够使得估计的跟踪结果置信度更加准确和鲁棒,置信度的估计结果可用于激活基于直方图和方差加权的目标搜索模块,并判断搜索窗口中是否包含目标.本文在目标跟踪标准数据集（Online object tracking benchmark,OTB）上对提出的算法和目前主流的目标跟踪算法进行对比实验,验证了本文提出算法的有效性和优越性.     

基于2DPCA与稀疏表示的目标跟踪

为了提高目标跟踪的准确性,针对目标跟踪过程中光照变化、遮挡、姿势变化等问题,提出了基于二维主成分分析(2DPCA)与稀疏表示的目标跟踪算法.在贝叶斯框架中使用了2DPCA与L2规范化呈现快速与鲁棒的目标跟踪算法.提出了新的似然函数表示方法,同时采用增量子空间学习的方法对冗余字典进行更新,有效抑制了跟踪漂移并能处理目标遮挡问题.通过对具有挑战性的跟踪视频进行定性和定量分析,实验结果证明:跟踪方法在跟踪精度上优于传统方法.     

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.     

Robust object tracking with RGBD-based sparse learning

Robust object tracking has been an important and challenging research area in the field of computer vision for decades. With the increasing popularity of affordable depth sensors, range data is widely used in visual tracking for its ability to provide robustness to varying illumination and occlusions. In this paper, a novel RGBD and sparse learning based tracker is proposed. The range data is integrated into the sparse learning framework in three respects. First, an extra depth view is added to the color image based visual features as an independent view for robust appearance modeling. Then, a special occlusion template set is designed to replenish the existing dictionary for handling various occlusion conditions. Finally, a depth-based occlusion detection method is proposed to efficiently determine an accurate time for the template update. Extensive experiments on both KITTI and Princeton data sets demonstrate that the proposed tracker outperforms the state-of-the-art tracking algorithms, including both sparse learning and RGBD based methods.     

抗遮挡目标跟踪的模型学习综述

视觉目标跟踪任务中的遮挡问题是最具挑战的场景属性之一,研究有效的抗遮挡模型学习方案,对构建适应复杂场景的长期鲁棒跟踪模型具有重要意义.剖析了遮挡影响跟踪性能的本质原因,以抗遮挡性能较好的先进跟踪算法为研究对象,系统分析了模型学习中有效抗遮挡机制,并对其改善长短期遮挡问题的有效性进行比较分析,包括以硬负样本挖掘、有效样本...     

Real-time shape tracking of facial landmarks

Detection of facial landmarks and accurate tracking of their shape are essential in real-time applications such as virtual makeup, where users can see the makeup’s effect by moving their face in diverse directions. Typical face tracking techniques detect facial landmarks and track them using a point tracker such as the Kanade-Lucas-Tomasi (KLT) point tracker. Typically, 5 or 64 points are used for tracking a face. Even though these points are enough to track the approximate locations of facial landmarks, they are not sufficient to track the exact shape of facial landmarks. In this paper, we propose a method that can track the exact shape of facial landmarks in real-time by combining a deep learning technique and a point tracker. We detect facial landmarks accurately using SegNet, which performs semantic segmentation based on deep learning. Edge points of detected landmarks are tracked using the KLT point tracker. In spite of its popularity, the KLT point tracker suffers from the point loss problem. We solve this problem by executing SegNet periodically to recalculate the shape of facial landmarks. That is, by combining the two techniques, we can avoid the computational overhead of SegNet and the point loss problem of the KLT point tracker, which leads to accurate real-time shape tracking. We performed several experiments to evaluate the performance of our method and report some of the results herein.

     

A framework for spatiotemporal control in the tracking of visual contours

There has been a great deal of research interest in contour tracking over the last five years. This article combines themes from tracking theory—elastic models and stochastic filtering—with the notion of affine invariance to synthesize a substantially new and demonstrably effective framework for contour tracking.A mechanism is developed for incorporating a shape template into a contour tracker via an affine invariant coupling. In that way the tracker becomes selective for shape and therefore able to ignore background clutter. Affine invariance ensures that the effect of varying viewpoint is accommodated. Use of a standard statistical filtering framework allows uncertainties to be treated systematically, which accommodates object flexibility and un-modeled distortions such as the deformation of a silhouette under motion.The statistical framework also facilitates a further development. In place of heuristically determined spatial scale for feature search, both spatial scale and temporal memory are controlled automatically and in a way that is responsive to the tracking process. Typically, the tracker operates initially in a coarse scale/short memory mode while it searches for a feature. Then spatial scale diminishes to allow more precise localization while memory (temporal scale) lengths to take advantage of motion coherence. All system parameters are determined by natural assumptions and desired tracking performance, leaving none to be fixed heuristically.Versions of the tracker have been implemented at video rate, both on SUN 4 and in parallel, using a network of 11 transputers. The theoretically established properties of automatic control of spatiotemporal scale and of affine invariance are demonstrated using the implemented tracker.     

变分调整约束下的反向低秩稀疏学习目标跟踪

目的 低秩稀疏学习目标跟踪算法在目标快速运动和严重遮挡等情况下容易出现跟踪漂移现象,为此提出一种变分调整约束下的反向低秩稀疏学习目标跟踪算法。方法 采用核范数凸近似低秩约束描述候选粒子间的时域相关性,去除不相关粒子,适应目标外观变化。通过反向稀疏表示描述目标表观,用候选粒子稀疏表示目标模板,减少在线跟踪中L₁优化问题的数目,提高跟踪效率。在有界变差空间利用变分调整对稀疏系数差分建模,约束目标表观在相邻帧间具有较小变化,但允许连续帧间差异存在跳跃不连续性,以适应目标快速运动。结果 实验利用OTB（object tracking benchmark）数据集中的4组涵盖了严重遮挡、快速运动、光照和尺度变化等挑战因素的标准视频序列进行测试,定性和定量对比了本文算法与5种热点算法的跟踪效果。定性分析基于视频序列的主要挑战因素进行比较,定量分析通过中心点位置误差（central pixel error,CPE）比较跟踪算法的精度。与CNT（convolutional networks training）、SCM（sparse collaborative model）、IST（inverse sparse tracker）、DDL（discriminative dictionary learning）和LLR（locally low-rank representation）算法相比,平均CPE值分别提高了2.80、4.16、13.37、35.94和41.59。实验结果表明,本文算法达到了较高的跟踪精度,对上述挑战因素更具鲁棒性。结论 本文提出的跟踪算法,综合了低秩稀疏学习和变分优化调整的优势,在复杂场景下具有较高的跟踪精度,特别是对严重遮挡和快速运动情况的有效跟踪更具鲁棒性。     

自适应尺度特征融合与模型更新的跟踪算法

在核相关滤波器跟踪算法中,为了减少背景相似物等杂波对跟踪器的干扰,以及解决不同跟踪结果置信度下的模型更新问题,提出了自适应尺度特征融合与模型更新的跟踪算法。通过多特征融合和尺度变化策略改进了多特征的尺度核相关滤波器,使用多峰检测对响应图的整体振荡程度进行判断,再对峰值进行跟踪结果置信度评估;在遮挡、形变等跟踪结果置信度低的情况下及时停止模型更新,在高置信度模型更新时,引入初始模型进行对齐操作,减少模型的更新误差,抑制模型漂移。比较核相关滤波器算法,本算法准确度较高,且在目标尺度变化、遮挡和形变时稳定性更好。在OTB-50数据集上的实验结果表明,该算法在精度和成功率上都比核相关滤波器算法表现更优。     

一种基于核相关滤波的视觉跟踪算法

视觉跟踪是计算机视觉的一个重要方向,而核相关滤波(KCF)跟踪是视觉跟踪领域中的一种比较新颖的方法,它不同于传统基于目标特征的方法,不仅具有较高的跟踪精度,而且具有较快的跟踪速度,在实际应用中效果显著。但当物体快速运动或存在较大尺度变化等时,该方法无法准确地跟踪目标。文中提出的基于核相关滤波器的改进算法有效地解决了上述问题,其通过随机更新多模板匹配,确定了核相关滤波的学习因子,从而实现了学习因子自适应更新模型。实验结果表明,该算法根据不同的场景能快速地调整学习因子,从而提高跟踪的成功度。通过自适应学习因子和多模板匹配,该算法对部分遮挡、光照和目标尺度变化具有较强的适应性。     

通道加权和自适应模型更新的实时目标跟踪

针对单一特征不能有效建模目标及特征通道直接叠加影响跟踪质量的问题,基于判别式尺度空间跟踪（DSST）提出了自适应模型更新与响应加权的实时相关滤波跟踪算法。首先,融合多维特征,并对每一响应通道自适应加权;其次,在尺度估计中融入多项式拟合策略;最后,根据响应图峰值波动情况进行样本可靠性判定,并提出模型更新策略。在OTB50和OTB100上进行实验,所提算法相较基准跟踪器,成功率分别提升6.4%和6.7%。与最近的跟踪器相比,其展现了优秀的性能,每一模块都对结果产生了有效的提升,且速度高达85 fps,是基准跟踪器DSST的三倍,超过大部分实时性跟踪算法。     

低视点下遮挡自适应感知的多目标跟踪算法

目的 针对低视点多目标跟踪场景的遮挡问题,提出一种能够遮挡自适应感知的多目标跟踪算法。方法 首先根据每帧图像的全局遮挡状态,提出了“自适应抗遮挡特征”,增强目标特征对遮挡的感知和调整能力。同时,采用“级联筛查机制”,减少由遮挡带来的目标特征剧烈变化而认定为“虚新入目标”的错误跟踪现象。最后,考虑到历史模板库中存在遮挡的模板对跟踪性能的影响,根据每一帧中目标的局部遮挡状态,提出自适应干扰模板更新机制,进一步提高对遮挡的应变和适应能力。结果 实验结果表明,本文算法在MOTA(multiple object tracking accuracy)、M OTP (multiple object tracking precision)、FN(false negatives)、Rcll (recall)、ML (mostly lost tracklets)等指标上明显优于STAM(spatial-temporal attention mechanism)、ATAF(aggregate tracklet appearance features)、STRN (spatial-temporal relat...     

改进的KCF算法在车辆跟踪中的应用

针对核相关滤波算法(KCF)在复杂道路场景下难以应对因车辆尺度变化,遮挡及旋转而不能继续跟踪的问题,提出了一种新的跟踪方法来更好地实现复杂道路场景下的车辆跟踪。该方法借鉴快速分类尺度空间跟踪器(fDDST),采用一维尺度相关滤波器进行尺度估计。同时融合Kalman滤波器形成预测-跟踪-校准的跟踪机制。该机制结合遮挡处理能够保证系统在目标被严重遮挡时跟踪的准确性。在模型更新方面,在目标被遮挡时,自适应的调节学习率参数,及时纠正模型偏移、特征丢失等问题。实验结果表明,在复杂道路场景下车辆旋转 、遮挡及尺度变化时,均能有效地跟踪目标车辆,且具有良好的鲁棒性。