Robust visual tracking via discriminative appearance model based on sparse coding

In this paper, we formulate visual tracking as a binary classification problem using a discriminative appearance model. To enhance the discriminative strength of the classifier in separating the object from the background, an over-complete dictionary containing structure information of both object and background is constructed which is used to encode the local patches inside the object region with sparsity constraint. These local sparse codes are then aggregated for object representation, and a classifier is learned to discriminate the target from the background. The candidate sample with largest classification score is considered as the tracking result. Different from recent sparsity-based tracking approaches that update the dictionary using a holistic template, we introduce a selective update strategy based on local image patches which alleviates the visual drift problem, especially when severe occlusion occurs. Experiments on challenging video sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.     

Robust visual tracking and vehicle classification via sparse representation

In this paper, we propose a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework. In this framework, occlusion, noise, and other challenging issues are addressed seamlessly through a set of trivial templates. Specifically, to find the tracking target in a new frame, each target candidate is sparsely represented in the space spanned by target templates and trivial templates. The sparsity is achieved by solving an l1-regularized least-squares problem. Then, the candidate with the smallest projection error is taken as the tracking target. After that, tracking is continued using a Bayesian state inference framework. Two strategies are used to further improve the tracking performance. First, target templates are dynamically updated to capture appearance changes. Second, nonnegativity constraints are enforced to filter out clutter which negatively resembles tracking targets. We test the proposed approach on numerous sequences involving different types of challenges, including occlusion and variations in illumination, scale, and pose. The proposed approach demonstrates excellent performance in comparison with previously proposed trackers. We also extend the method for simultaneous tracking and recognition by introducing a static template set which stores target images from different classes. The recognition result at each frame is propagated to produce the final result for the whole video. The approach is validated on a vehicle tracking and classification task using outdoor infrared video sequences.     

判别稀疏表示鲁棒快速视觉跟踪

目的 L1跟踪对局部遮挡具有良好的鲁棒性,但存在易产生模型漂移和计算速度慢的问题。针对这两个问题,该文提出了一种基于判别稀疏表示的视觉跟踪方法。方法 考虑到背景和遮挡信息的干扰,提出了一种判别稀疏表示模型,并基于块坐标优化原理,采用学习迭代收缩阈值算法和软阈值操作设计出了表示模型的快速求解算法。结果 在8组图像序列中,该文方法与现有的4种经典跟踪方法分别在鲁棒性和稀疏表示的计算时间方面进行了比较。在鲁棒性的定性和定量比较实验中,该文方法不仅表现出了对跟踪过程中的多种干扰因素具有良好的适应能力,而且在位置误差阈值从0~50像素的变化过程中,其精度曲线均优于实验中的其他方法;在稀疏表示的计算时间方面,在采用大小为16×16和32×32的模板进行跟踪时,该文算法的时间消耗分别为0.152 s和0.257 s,其时效性明显优于实验中的其他方法。结论 与经典的跟踪方法相比,该文方法能够在克服遮挡、背景干扰和外观改变等诸多不良因素的同时,实现快速目标跟踪。由于该文方法不仅具有较优的稀疏表示计算速度,而且能够克服多种影响跟踪鲁棒性的干扰因素,因此可以将其应用于视频监控和体育竞技等实际场合。     

Discriminative subspace learning with sparse representation view-based model for robust visual tracking

In this paper, we propose a robust tracking algorithm to handle drifting problem. This algorithm consists of two parts: the first part is the G&D part that combines Generative model and Discriminative model for tracking, and the second part is the View-Based model for target appearance that corrects the result of the G&D part if necessary. In G&D part, we use the Maximum Margin Projection (MMP) to construct a graph model to preserve both local geometrical and discriminant structures of the data manifold in low dimensions. Therefore, such discriminative subspace combined with traditional generative subspace can benefit from both models. In addition, we address the problem of learning maximum margin projection under the Spectral Regression (SR) which results in significant savings in computational time. To further solve the drift, an online learned sparsely represented view-based model of the target is complementary to the G&D part. When the result of G&D part is unreliable, the view-based model can rectify the result in order to avoid drifting. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our approach.     

Object tracking via appearance modeling and sparse representation

This paper proposes a robust tracking method by the combination of appearance modeling and sparse representation. In this method, the appearance of an object is modeled by multiple linear subspaces. Then within the sparse representation framework, we construct a similarity measure to evaluate the distance between a target candidate and the learned appearance model. Finally, tracking is achieved by Bayesian inference, in which a particle filter is used to estimate the target state sequentially over time. With the tracking result, the learned appearance model will be updated adaptively. The combination of appearance modeling and sparse representation makes our tracking algorithm robust to most of possible target variations due to illumination changes, pose changes, deformations and occlusions. Theoretic analysis and experiments compared with state-of-the-art methods demonstrate the effectivity of the proposed algorithm.     

Robust tracking with interest points: A sparse representation approach

Visual tracking is an important task in various computer vision applications including visual surveillance, human computer interaction, event detection, video indexing and retrieval. Recent state of the art sparse representation (SR) based trackers show better robustness than many of the other existing trackers. One of the issues with these SR trackers is low execution speed. The particle filter framework is one of the major aspects responsible for slow execution, and is common to most of the existing SR trackers. In this paper,¹ we propose a robust interest point based tracker in l₁ minimization framework that runs at real-time with performance comparable to the state of the art trackers. In the proposed tracker, the target dictionary is obtained from the patches around target interest points. Next, the interest points from the candidate window of the current frame are obtained. The correspondence between target and candidate points is obtained via solving the proposed l₁ minimization problem.In order to prune the noisy matches, a robust matching criterion is proposed, where only the reliable candidate points that mutually match with target and candidate dictionary elements are considered for tracking. The object is localized by measuring the displacement of these interest points. The reliable candidate patches are used for updating the target dictionary. The performance and accuracy of the proposed tracker is benchmarked with several complex video sequences. The tracker is found to be considerably fast as compared to the reported state of the art trackers. The proposed tracker is further evaluated for various local patch sizes, number of interest points and regularization parameters. The performance of the tracker for various challenges including illumination change, occlusion, and background clutter has been quantified with a benchmark dataset containing 50 videos.     

Robust online appearance models for visual tracking

We propose a framework for learning robust, adaptive, appearance models to be used for motion-based tracking of natural objects. The model adapts to slowly changing appearance, and it maintains a natural measure of the stability of the observed image structure during tracking. By identifying stable properties of appearance, we can weight them more heavily for motion estimation, while less stable properties can be proportionately downweighted. The appearance model involves a mixture of stable image structure, learned over long time courses, along with two-frame motion information and an outlier process. An online EM-algorithm is used to adapt the appearance model parameters over time. An implementation of this approach is developed for an appearance model based on the filter responses from a steerable pyramid. This model is used in a motion-based tracking algorithm to provide robustness in the face of image outliers, such as those caused by occlusions, while adapting to natural changes in appearance such as those due to facial expressions or variations in 3D pose.     

具有动态弹性稀疏表示的鲁棒目标跟踪算法

目标跟踪问题中目标所在环境的变化对跟踪效果有较大影响.鉴于此,提出一种基于弹性网结构的稀疏表示模型,井在粒子滤波框架下设计一种应用稀疏表示模型的抗干扰动态弹性网目标跟踪算法.同时,设计一种根据环境变化程度动态更新稀疏表示模型参数的方法,以克服光照变化等干扰对算法跟踪质量的影响.此外,所提出算法通过使用各向异性核函数计算...     

Robust visual tracking using adaptive local appearance model for smart transportation

Multimedia Tools and Applications - Smart transportation plays an important role in building smart cities. We can obtain mass data from multi-source and use it to manage transportation in an...     

改进型稀疏分类鲁棒目标跟踪算法

针对当前基于稀疏分类的目标跟踪算法跟踪精度较低等问题,结合判别分析思想,提出改进型稀疏跟踪算法。采用基于在线学习的标准对冲算法估算目标的位置以及面积,并详细介绍了标准对冲算法原理。对于在跟踪过程中目标外形改变的问题,提出了基于时序循环的模板更新方法。对目标暂时消失或被完全遮挡时会产生跟踪失败的问题,创造性地提出了基于稀疏分类器网格SCG的合作跟踪框架。进行了两类实验,第一类实验验证了该算法的有效性。第二类实验在大量公共图像序列的基础上对该算法及其他图像跟踪算法进行测试比较。实验结果证明,该算法适用于复杂背景下的跟踪任务,在跟踪失败后能自动恢复跟踪,在目标被部分遮挡、长期遮挡或目标与背景有相似特征模式的情况下都能保持较高的跟踪精度。     

改进稀疏表示模型的目标跟踪

针对受到光照、遮挡及姿态变化等引起的目标外观发生变化时,目标跟踪的鲁棒性和准确性较差的问题,将稀疏表示引入到粒子滤波框架进行目标跟踪,提出一种稀疏协同模型。首先,在目标运动定位模型中,使用灰度强度值表示目标对象;其次,判别模型通过训练正负模板集获得最优分类特征,并在生成模型中对目标直方图加权以提高目标生成效率;然后,将分类判别模型和生成模型集成在协同模型中,利用重构误差确定目标;最后,通过各模块独立更新,减少目标外观变化对目标跟踪的影响。实验结果表明,所提方法的平均中心误差仅为7.5像素,且具备良好的抗噪性和实时性。     

判别稀疏表示与在线字典学习的运动目标跟踪

针对传统稀疏表示不能有效区分目标和背景的缺点,提出一种判别稀疏表示算法,这种算法在传统稀疏表示目标函数中加入一个判别函数,大大降低干扰因素对目标跟踪的影响。基于判别稀疏表示和[?1]约束,提出一种在线字典学习算法升级目标模板,有效降低背景信息对目标模板的影响。提取目标梯度方向的直方图（HOG）特征,利用其对光照和形变等复杂环境具有较强鲁棒性的优点,实现对目标更稳定的跟踪。实验结果表明,与现有跟踪方法相比,该算法的跟踪效果更好。     

Robust object tracking based on sparse representation and incremental weighted PCA

Multimedia Tools and Applications - Object tracking plays a crucial role in many applications of computer vision, but it is still a challenging problem due to the variations of illumination, shape...     

Multi-scale patch-based sparse appearance model for robust object tracking

When objects undergo large pose change, illumination variation or partial occlusion, most existing visual tracking algorithms tend to drift away from targets and even fail to track them. To address the issue, in this paper we propose a multi-scale patch-based appearance model with sparse representation and provide an efficient scheme involving the collaboration between multi-scale patches encoded by sparse coefficients. The key idea of our method is to model the appearance of an object by different scale patches, which are represented by sparse coefficients with different scale dictionaries. The model exploits both partial and spatial information of targets based on multi-scale patches. Afterwards, a similarity score of one candidate target is input into a particle filter framework to estimate the target state sequentially over time in visual tracking. Additionally, to decrease the visual drift caused by frequently updating model, we present a novel two-step object tracking method which exploits both the ground truth information of the target labeled in the first frame and the target obtained online with the multi-scale patch information. Experiments on some publicly available benchmarks of video sequences showed that the similarity involving complementary information can locate targets more accurately and the proposed tracker is more robust and effective than others.     

融合残差Unscented粒子滤波和区别性稀疏表示的鲁棒目标跟踪

（目的）提出一种鲁棒的目标跟踪算法,将区别性稀疏表示模型应用于残差Unscented粒子滤波(RUPF)跟踪框架,从而实现对目标高效准确的跟踪。（方法）利用Unscented卡尔曼(UKF)滤波技术将目标的量测信息引入提议分布,并使用马尔科夫蒙特卡洛(MCMC)移动改进采样结果,提高了滤波的精度,同时有效防止了粒子的退化和贫化。基于稀疏表示建立区别性的目标观测模型,引入的背景成分可以增强算法分辨目标与背景的能力。采用可变方向乘子法(ADMM)解决稀疏表示中的L1优化问题,有效的提升了算法的执行效率。（结果）通过和其它跟踪算法一起对标准测试视频进行的大量定性与定量的实验表明,本文提出的跟踪算法的跟踪精度高于一些常见的跟踪算法,同时其时间复杂度低于传统的几种基于稀疏的跟踪算法。（结论）随着硬件技术的不断发展,UKF滤波技术的速度不断提升,保证了本文算法可以在较高准确率下有更快的执行速度。     

Visual tracking using structural local DCT sparse appearance model with occlusion detection

Multimedia Tools and Applications - In this paper, a structural local DCT sparse appearance model with occlusion detection is proposed for visual tracking in a particle filter framework. The energy...     

Robust object tracking using a sparse coadjutant observation model

Multimedia Tools and Applications - This paper develops a classical visual tracker that is called a discriminative sparse similarity (DSS) tracker. Based on the classical Laplacian multi-task...     

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.     

基于自适应表面模型的概率视频跟踪算法

提出一种鲁棒自适应表面模型,该模型中每个像素值的变化过程由一混合高斯分布描述．为了适应目标表面的变化,这些高斯参数在跟踪期间通过在线的EM算法自适应更新;在估计目标状态时。采用了粒子滤波算法。设计了基于自适应表面模型的观测模型;在处理遮挡时,采用了一种鲁棒估计技术．多组试验结果表明,该算法对光照变化、姿态变化、部分或完全遮挡下的跟踪具有较强的鲁棒性．