基于稀疏表达的超像素跟踪算法

该文针对真实场景下视频跟踪过程中可能出现的目标形变、运动和遮挡等问题,该文分别构建了基于超像素局部信息的判别式模型和基于颜色与梯度全局信息的产生式模型,通过两者的结合提升了目标表观特征描述的可区分性和不变性;此外,提出一种基于稀疏主成分分析的更新策略,在更新特征字典的同时减少其冗余度,在判别式模型的更新阶段分别对每帧图像获得的跟踪结果进行二次判别从而避免漂移现象的发生。实验结果表明,与其它跟踪算法相比,该算法在应对目标姿态变化、背景干扰以及遮挡等复杂情况时具有更好的稳定性和鲁棒性。     

Collaborative object tracking model with local sparse representation

There existed many visual tracking methods that are based on sparse representation model, most of them were either generative or discriminative, which made object tracking more difficult when objects have undergone large pose change, illumination variation or partial occlusion. To address this issue, in this paper we propose a collaborative object tracking model with local sparse representation. The key idea of our method is to develop a local sparse representation-based discriminative model (SRDM) and a local sparse representation-based generative model (SRGM). In the SRDM module, the appearance of a target is modeled by local sparse codes that can be formed as training data for a linear classifier to discriminate the target from the background. In the SRGM module, the appearance of the target is represented by sparse coding histogram and a sparse coding-based similarity measure is applied to compute the distance between histograms of a target candidate and the target template. Finally, a collaborative similarity measure is proposed for measuring the difference of the two models, and then the corresponding likelihood of the target candidates is input into a particle filter framework to estimate the target state sequentially over time in visual tracking. Experiments on some publicly available benchmarks of video sequences showed that our proposed tracker is robust and effective.     

Visual tracking via salient feature extraction and sparse collaborative model

Object tracking is always a very attractive research topic in computer vision and image processing. In this paper, an innovative method called salient-sparse-collaborative tracker (SSCT) is put forward, which exploits both object saliency and sparse representation. Within the proposed collaborative appearance model, the object salient feature map is built to create a salient-sparse discriminative model (SSDM) and a salient-sparse generative model (SSGM). In the SSDM module, the presented sparse model effectively distinguishes the target region from its background by using the salient feature map that further helps locate the object in complex environment. In the SSGM module, a sparse representation method with salient feature map is designed to improve the effectiveness of the templates and deal with occlusions. The update scheme takes advantage of salient correction, thus the SSCT algorithm can both handle the appearance variation as well as reduce tracking drifts effectively. Plenty of experiments with quantitative and qualitative comparisons on benchmark reveal the SSCT tracker is more competitive than several popular approaches.     

End-to-end DeepNCC framework for robust visual tracking

In this paper, we propose an NCC-based object tracking deep framework, which can be well initialized with the limited target samples in the first frame. The proposed framework contains a pretrained model, online feature fine-tuning layers and tracking processes. The pretrained model provides rich feature representations while online feature fine-tuning layers select discriminative and generic features for the tracked object. We choose normalized cross-correlation as a template tracking layer to perform the tracking process. To enable the learned features representation closely coordinated to the tracked target, we jointly train the feature representation network and tracking processes. In online tracking, an adaptive template and a fixed template are fused to find the optimal tracking results. Scale estimation and a high-confidence model update scheme are perfectly integrated into the framework to adapt to the target appearance changes. The extensive experiments demonstrate that the proposed tracker achieves superior performance compared with other state-of-the-art trackers.     

基于局部结构变换域稀疏外观模型的目标跟踪

[目的]为了降低稀疏表示目标跟踪算法的计算复杂度,[方法]在粒子滤波框架下提出了基于局部结构变换域稀疏外观模型的视觉目标跟踪算法.[结果]该算法在目标区域附近提取重叠的局部图像块,并计算出所有局部图像块的二维离散余弦变换,获得图像块的变换域系数.变换域的能量集中特性被采用来降低字典的维度与候选样本的数量,并且对系数压缩一定的自由度可以抑制噪声与遮挡影响.采用被裁剪的样本与字典获得局部图像块的稀疏编码,然后将当前目标区域中所有小图像块的稀疏向量加权融合得到目标区域的稀疏表示值,并通过决策模型获取最优跟踪结果.与现有三种最新的跟踪算法比较的实验结果表明,[结论]所提算法的跟踪性能接近或超过对比算法,同时大大减小了f1范数最小化的计算复杂度.     

基于稀疏表示多子模板的鲁棒目标跟踪算法

目标跟踪技术是一项富有挑战性的研究课题,在红外成像搜索、红外精确制导、智能监控、运动识别等领域有着广泛的应用。文中提出了一种基于稀疏表示多子模板的鲁棒目标跟踪算法。首先,提出一种基于自适应辨别信息的子模板选择方法,最大限度地捕捉目标的结构信息,提高模板子块的整体描述;针对直方图对光照敏感的缺点,引入了稀疏表示理论对子模板进行描述,提高模板子块的表达能力和适应能力;其次通过构造表决图的形式对目标位置进行表决和融合决策;最后设计了一种动态的子模板更新策略,来有效地应对目标外观模型的变化。在大量测试图像序列中的仿真实验表明,文中所提算法可以有效应对形变、光照变化、部分遮挡、完全遮挡以及虚假目标干扰和背景干扰,具有较高的鲁棒性。     

Robust tracking with discriminative ranking lists

In this paper, we propose a novel tracking algorithm, i.e., the discriminative ranking list-based tracker (DRLTracker). The DRLTracker models the target object and its local background by using ranking lists of patches of different scales within object bounding boxes. The ranking list of each of such patches is its K nearest neighbors. Patches of the same scale with ranking lists of high purity values (meaning high probabilities to be on the target object) and some confusable background patches constitute the object model under that scale. A pair of object models of two different scales collaborate to determine which patches may belong to the target object in the next frame. The DRLTracker can effectively alleviate the distraction problem, and its superior ability over several representative and state-of-the-art trackers is demonstrated through extensive experiments.     

Robust vision tracking by online random ferns and template library

In this paper, we proposed a robust tracking algorithm with an appearance model based on random ferns and template library. We adopt random Gaussian difference to generate binary features which depend on two randomly selected points and their corresponding Gaussian blur kernels. Semi-naive Bayes based random ferns are adopted as the discriminative model, and a template library including both positive templates and negative templates is used as generative model, the co-training of both discriminative and generative models gives our tracker the ability to separate foreground and background samples accurately. Besides, we also come up with a fragment based method which combines global ferns and local ferns to handle the occlusion problem. Experimental results demonstrated that the proposed algorithm performs well in terms of accuracy and robustness.     

Visual object tracking via online sparse instance learning

Sparse representation has been attracting much more attention in visual tracking. However most sparse representation based trackers only focus on how to model the target appearance and do not consider the learning of sparse representation when the training samples are imprecise, and hence may drift or fail in the challenging scene. In this paper, we present a novel online tracking algorithm. The tracker integrates the online multiple instance learning into the recent sparse representation scheme. For tracking, the integrated sparse representation combining texture, intensity and local spatial information is proposed to model the target. This representation takes both occlusion and appearance change into account. Then, an efficient online learning approach is proposed to select the most distinguishable features to separate the target from the background samples. In addition, the sparse representation is dynamically updated online with respect to the current context. Both qualitative and quantitative evaluations on challenging benchmark video sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.     

基于局部分块和模型更新的视觉跟踪算法

针对目标跟踪过程中的目标表观变化、背景干扰及发生遮挡等问题,该文提出一种基于局部分块和模型更新的视觉跟踪算法。该文采用粗搜索与精搜索相结合的双层搜索方法来提高目标的定位精度。首先,在包含部分背景区域的初始跟踪区域内构建目标模型。然后,利用基于积分直方图的局部穷搜索算法初步确定目标的位置,接着在当前跟踪区域内通过分块学习来精确搜索目标的最终位置。最后,利用创建的模型更新域对目标模型进行更新。该文主要针对分块跟踪中的背景抑制、模型更新等方面进行了研究,实验结果表明该算法对目标表观变化、背景干扰及遮挡情况的处理能力都有所增强。     

基于视觉显著图在线更新的结构反稀疏目标跟踪算法

为提高稀疏跟踪器性能,提出一种在贝叶斯推论框架下的基于视觉显著图的结构反稀疏在线目标跟踪算法。首先将基于马尔可夫(Markov)模型的关联性视觉显著度检测算法用于当前帧并计算目标模板的显著图,其次提出全局与局部分块的结构外观模型表示候选目标,将显著图映射回每一个局部块并计算出对应的自适应权重,最后提出联合全局与局部稀疏解的度量准则度量候选目标与目标模板的相似度,从而确立在贝叶斯框架下对目标状态最佳估计。在跟踪过程中,采用反稀疏表达方式一次求解优化问题计算出所有粒子权重来提高算法效率。实验结果表明,本文算法具有良好的鲁棒性和实时性。      

基于在线学习的压缩模版的目标跟踪算法

利用压缩感知(CS)矩阵建立压缩特征模版,通过在 线学习,实现一种更高效的目标跟踪算法。首先利用符 合有限等距性质的随机感知矩阵,取得跟踪目标表面特征的压缩模板;然后利用模板匹配 确定目标区域, 同时在跟踪过程中在线学习目标外观变化,并以此更新目标模板。采用了局部模板更新策略 ,使目标模板 更为准确,更能体现目标的变化状况。采用了模板匹配的确定性跟踪方法,大大减少了大量 正负样本的采 样,降低了算法复杂度。实验结果表明,与压缩跟踪(CT)算法相比,本文的基于在线学习的 压缩模版跟踪算法在时间性能 上有了明显提升,采用Kalman滤波器加速跟踪可提高6倍以上的时间性能,在多个标准测 试序列中的跟踪成功率也有了明显提高,有效地防止了跟踪中的漂移现象。     

Transferring visual prior for online object tracking

Visual prior from generic real-world images can be learned and transferred for representing objects in a scene. Motivated by this, we propose an algorithm that transfers visual prior learned offline for online object tracking. From a collection of real-world images, we learn an overcomplete dictionary to represent visual prior. The prior knowledge of objects is generic, and the training image set does not necessarily contain any observation of the target object. During the tracking process, the learned visual prior is transferred to construct an object representation by sparse coding and multiscale max pooling. With this representation, a linear classifier is learned online to distinguish the target from the background and to account for the target and background appearance variations over time. Tracking is then carried out within a Bayesian inference framework, in which the learned classifier is used to construct the observation model and a particle filter is used to estimate the tracking result sequentially. Experiments on a variety of challenging sequences with comparisons to several state-of-the-art methods demonstrate that more robust object tracking can be achieved by transferring visual prior.     

Object Tracking via Partial Least Squares Analysis

We propose an object tracking algorithm that learns a set of appearance models for adaptive discriminative object representation. In this paper, object tracking is posed as a binary classification problem in which the correlation of object appearance and class labels from foreground and background is modeled by partial least squares (PLS) analysis, for generating a low-dimensional discriminative feature subspace. As object appearance is temporally correlated and likely to repeat over time, we learn and adapt multiple appearance models with PLS analysis for robust tracking. The proposed algorithm exploits both the ground truth appearance information of the target labeled in the first frame and the image observations obtained online, thereby alleviating the tracking drift problem caused by model update. Experiments on numerous challenging sequences and comparisons to state-of-the-art methods demonstrate favorable performance of the proposed tracking algorithm.     

基于稀疏稠密结构表示与在线鲁棒字典学习的视觉跟踪

L1跟踪对适度的遮挡具有鲁棒性,但是存在速度慢和易产生模型漂移的不足。为了解决上述两个问题,该文首先提出一种基于稀疏稠密结构的鲁棒表示模型。该模型对目标模板系数和小模板系数分别进行L2范数和L1范数正则化增强了对离群模板的鲁棒性。为了提高目标跟踪速度,基于块坐标优化原理,用岭回归和软阈值操作建立了该模型的快速算法。其次,为降低模型漂移的发生,该文提出一种在线鲁棒的字典学习算法用于模板更新。在粒子滤波框架下,用该表示模型和字典学习算法实现了鲁棒快速的跟踪方法。在多个具有挑战性的图像序列上的实验结果表明:与现有跟踪方法相比,所提跟踪方法具有较优的跟踪性能。     

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%。     

Tracking with dynamic weighted compressive model

Fast compressive tracking utilizes a very sparse measurement matrix to capture the appearance model of targets. Such model performs well when the tracked targets are well defined. However, when the targets are low-grain, low-resolution, or small, a single fixed size sparse measurement matrix is not sufficient enough to preserve the image structure of the target. In this work, we propose a multi-sparse measurement matrices scheme along with a weight map to select the best measurement matrix that preserves the image structure of the targets during tracking. The weight map combines a contrast weight and a feature weight to efficiently characterize the target appearance and location. Moreover, a dispersion function is used for the online update of the target template, allowing tracking both the location and scale of the target. Extensive experimental results have demonstrated that the proposed DWCM tracking algorithm outperforms several state-of-the-art tracking algorithms as well as compressive tracker.     

Structural sparse coding seeds–active appearance model for object tracking

In this paper, we propose a tracking algorithm that can robustly handle appearance variations in tracking process. Our method is based on seeds–active appearance model, which is composed by structural sparse coding. In order to compensate for illumination changes, heavy occlusion and appearance self-updating problem, we proposed a mixture online learning scheme for modeling the target object appearance model. The proposed object tracking scheme involves three stages: training, detection and tracking. In the training stage, an incremental SVM model that directly measures the candidates samples and target difference. The proposed mixture generate–discriminative method can well separate two highly correlated positive candidates images. In the detection stage, the trained weighted vector is used to separate the target object in positive candidates images with respect to the seeds images. In the tracking stage, we employ the particle filter to track the object through an appearance adaptive updating algorithm with seeds–active constrained sparse representation. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than alternatives reported in the current literature.     

局部感知下的稀疏优化目标跟踪方法

针对传统稀疏表示跟踪算法在复杂背景中易出现跟踪漂移问题,该文提出一种局部感知下的稀疏优化目标跟踪方法。首先,将首帧确定的目标区域进行非重叠均匀分割,并利用目标的全局特征和局部特征联合建模。然后,提出一种局部感知校验方法约束稀疏优化匹配过程,从而确定最优匹配样本。最后,在模板更新中提出一种决策方法对遮挡进行检测,并针对不同遮挡情况采取相应的更新策略,使得更新后的模板集更加完善。实验在10个标准库视频序列中测试,并与目前较流行的目标跟踪算法在跟踪效果、成功率等方面进行比较,实验结果表明,提出的跟踪方法在局部遮挡、目标形变、复杂背景等条件下跟踪准确、适应性强。     

融合局部加权余弦与稀疏表示的目标跟踪算法

针对目标跟踪算法的鲁棒性难题,在粒子滤波框架下提出基于联合模型的目标跟踪算法。首先,由局部加权余弦相似对目标模板和候选目标进行匹配,其中的局部加权算法增加了未受遮挡、形变等影响的候选目标的权重;其次,通过对目标区域局部图像块稀疏编码来表示目标观测模型,其中字典不进行更新,重建误差的构建考虑了局部图像块之间的空间布局;最后,利用最大后验概率估计目标状态。联合模型将目标的当前状态和原始状态都考虑在内,提高了观测模型的可靠性。实验结果表明,该算法具有较强的鲁棒性。