字典学习联合粒子滤波鲁棒跟踪

针对运动目标鲁棒跟踪问题,提出一种基于离线字典学习的视频目标跟踪鲁棒算法。采用字典编码方式提取目标的局部区域描述符,随后通过训练分类器将跟踪问题转化为背景和前景分类问题,最终通过粒子滤波对物体位置进行估计实现跟踪。该算法能够有效解决由于光照变化、背景复杂、快速运动、遮挡产生的跟踪困难。经过不同图像序列的实验对比表明,与现有方法相比,本文算法的鲁棒性较高。     

基于字典学习的粒子滤波鲁棒跟踪算法

字典学习广泛应用于图像去噪、图像分类等领域,但是将离线字典训练如何应用于视频目标跟踪的研究较少。本文采用一种字典编码方法提取目标的局部区域描述符,通过训练分类器将跟踪问题转化为背景和前景二值分类问题,并通过粒子滤波对物体位置进行估计实现跟踪。不同图像序列的实验结果表明,与现有的方法相比本文的算法具有较好的鲁棒性。     

Convolutional restricted Boltzmann machines learning for robust visual tracking

It is a critical step to choose visual features in object tracking. Most existing tracking approaches adopt handcrafted features, which greatly depend on people’s prior knowledge and easily become invalid in other conditions where the scene structures are different. On the contrary, we learn informative and discriminative features from image data of tracking scenes itself. Local receptive filters and weight sharing make the convolutional restricted Boltzmann machines (CRBM) suit for natural images. The CRBM is applied to model the distribution of image patches sampled from the first frame which shares same properties with other frames. Each hidden variable corresponding to one local filter can be viewed as a feature detector. Local connections to hidden variables and max-pooling strategy make the extracted features invariant to shifts and distortions. A simple naive Bayes classifier is used to separate object from background in feature space. We demonstrate the effectiveness and robustness of our tracking method in several challenging video sequences. Experimental results show that features automatically learned by CRBM are effective for object tracking.     

背景与时间感知的相关滤波实时视觉跟踪

目的 传统的相关滤波跟踪算法采用对跟踪目标（唯一准确正样本）循环移位获取负样本,在整个学习过程中没有对真正的背景信息进行建模,因此当目标与背景信息极其相似时容易漂移。大多数跟踪算法为了提高跟踪性能,在时间序列上收集了大量的训练样本而导致计算复杂度的增加。采用模型在线更新策略,由于未考虑时间一致性,使得学习到的滤波器可能偏向背景而发生漂移。为了改善以上问题,本文在背景感知相关滤波（BACF）跟踪算法的基础上,加入时间感知,构建了一个带等式限制的相关滤波目标函数,称为背景与时间感知相关滤波（BTCF）视觉跟踪。该算法不但获取了真正的负样本作为训练集,而且仅用当前帧信息无需模型在线更新策略就能学习到具有较强判别力的相关滤波器。方法 首先将带等式限制的相关滤波目标函数转化为无约束的增广拉格朗日乘子公式,然后采用交替方向乘子方法（ADMM）转化为两个具有闭式解的子问题迭代求最优解。结果 采用OTB2015数据库中的OPE（one pass evaluation）评价准则,以成功率曲线图线下面积（AUC）和中心点位置误差为评判标准,在OTB2015公开数据库上与10个比较优秀的视觉跟踪算法进行对比实验。结果显示,100个视频序列和11个视频属性的成功率及对应的AUC和中心位置误差均明显优于其他基于相关滤波的视觉跟踪算法,说明本文算法具有良好的跟踪效果。本文的BTCF算法仅采用HOG纯手工特征,在OTB2015数据库上AUC较BACF算法提高了1.3%;由于颜色与边缘特征具有互补特性,本文融合CN（color names）特征后,在OTB2015数据库上,AUC较BACF算法提高了4.2%,采用纯手工特征跟踪性能AUC达到0.663,跟踪速度达到25.4帧/s。结论 本文的BTCF算法能够适用于光照变化、目标旋转、遮挡等复杂情况下的视觉跟踪,具有良好的鲁棒性和一定的实时性。     

Discriminative multi-task objects tracking with active feature selection and drift correction

In this paper, we propose a discriminative multi-task objects tracking method with active feature selection and drift correction. The developed method formulates object tracking in a particle filter framework as multi-Task discriminative tracking. As opposed to generative methods that handle particles separately, the proposed method learns the representation of all the particles jointly and the corresponding coefficients are similar. The tracking algorithm starts from the active feature selection scheme, which adaptively chooses suitable number of discriminative features from the tracked target and background in the dynamic environment. Based on the selected feature space, the discriminative dictionary is constructed and updated dynamically. Only a few of them are used to represent all the particles at each frame. In other words, all the particles share the same dictionary templates and their representations are obtained jointly by discriminative multi-task learning. The particle that has the highest similarity with the dictionary templates is selected as the next tracked target state. This jointly sparsity and discriminative learning can exploit the relationship between particles and improve tracking performance. To alleviate the visual drift problem encountered in object tracking, a two-stage particle filtering algorithm is proposed to complete drift correction and exploit both the ground truth information of the first frame and observations obtained online from the current frame. Experimental evaluations on challenging sequences demonstrate the effectiveness, accuracy and robustness of the proposed tracker in comparison with state-of-the-art algorithms.     

Online unsupervised feature learning for visual tracking

We propose a method for visual tracking-by-detection based on online feature learning. Our learning framework performs feature encoding with respect to an over-complete dictionary, followed by spatial pyramid pooling. We then learn a linear classifier based on the resulting feature encoding. Unlike previous work, we learn the dictionary online and update it to help capture the appearance of the tracked target as well as the background. In more detail, given a test image window, we extract local image patches from it and each local patch is encoded with respect to the dictionary. The encoded features are then pooled over a spatial pyramid to form an aggregated feature vector. Finally, a simple linear classifier is trained on these features.Our experiments show that the proposed powerful—albeit simple—tracker, outperforms all the state-of-the-art tracking methods that we have tested. Moreover, we evaluate the performance of different dictionary learning and feature encoding methods in the proposed tracking framework, and analyze the impact of each component in the tracking scenario. In particular, we show that a small dictionary, learned and updated online is as effective and more efficient than a huge dictionary learned offline. We further demonstrate the flexibility of feature learning by showing how it can be used within a structured learning tracking framework. The outcome is one of the best trackers reported to date, which facilitates the advantages of both feature learning and structured output prediction. We also implement a multi-object tracker, which achieves state-of-the-art performance.     

协同结构稀疏重构的判别性视觉跟踪

基于稀疏表示的表观似然模型在目标跟踪领域具有广泛的应用,但是这种单一产生式目标表观模型并未考虑完整的判别性结构信息,容易受复杂背景的干扰。为了缓解由该问题造成的目标跟踪漂移,提出了一种目标表观字典和背景字典协同结构稀疏重构优化的视觉跟踪方法。通过构建一个有判别力的基于稀疏表示的表观似然模型,实现了对目标表观模型更为准确的描述。通过合理选择约束候选目标区域和候选背景区域的稀疏系数,在表观似然模型中引入判别式信息,以进一步揭示候选目标区域的潜在相关性和候选背景区域的结构关系,从而更加准确地学习候选目标区域的表观模型。大量有挑战性的视频序列上的实验结果验证了算法在复杂背景下跟踪的鲁棒性,与其他相关算法的对比实验也体现了该算法的优越性。     

Visual tracking based on hierarchical framework and sparse representation

As the main challenge for object tracking is to account for drastic appearance change, a hierarchical framework that exploits the strength of both generative and discriminative models is devised in this paper. Our hierarchical framework consists of three appearance models: local-histogram-based model, weighted alignment pooling model, and sparsity-based discriminative model. Sparse representation is adopted in local-histogram-based model layer that considers the spatial information among local patches with a dual-threshold update schema to deal with occlusion. The weighted alignment pooling layer is introduced to weight the local image patches of the candidates after sparse representation. Different from the above two generative methods, the global discriminant model layer employs candidates to sparsely represent positive and negative templates. After that, an effective hierarchical fusion strategy is developed to fuse the three models via their similarities and the confidence. In addition, three reasonable online dictionary and template update strategies are proposed. Finally, experiments on various current popular image sequences demonstrate that our proposed tracker performs favorably against several state-of-the-art algorithms.     

Object tracking via collaborative multi-task learning and appearance model updating

In this paper, we propose a novel visual tracking algorithm using the collaboration of generative and discriminative trackers under the particle filter framework. Each particle denotes a single task, and we encode all the tasks simultaneously in a structured multi-task learning manner. Then, we implement generative and discriminative trackers, respectively. The discriminative tracker considers the overall information of object to represent the object appearance; while the generative tracker takes the local information of object into account for handling partial occlusions. Therefore, two models are complementary during the tracking. Furthermore, we design an effective dictionary updating mechanism. The dictionary is composed of fixed and variational parts. The variational parts are progressively updated using Metropolis–Hastings strategy. Experiments on different challenging video sequences demonstrate that the proposed tracker performs favorably against several state-of-the-art trackers.     

Learning structured visual dictionary for object tracking

In this paper, we propose a visual tracking algorithm by incorporating the appearance information gathered from two collaborative feature sets and exploiting its geometric structures. A structured visual dictionary (SVD) can be learned from both appearance and geometric structure, thereby enhancing its discriminative strength between the foreground object and the background. Experimental results show that the proposed tracking algorithm using SVD (SVDTrack) performs favorably against the state-of-the-art methods.     

Robust visual tracking via online semi-supervised co-boosting

This paper proposes a novel visual tracking algorithm via online semi-supervised co-boosting, which investigates the benefits of co-boosting (i.e., the integration of co-training and boosting) and semi-supervised learning in the online tracking process. Existing discriminative tracking algorithms often use the classification results to update the classifier itself. However, the classification errors are easily accumulated during the self-training process. In this paper, we employ an effective online semi-supervised co-boosting framework to update the weak classifiers built on two different feature views. In this framework, the pseudo-label and importance of an unlabeled sample are estimated based on the additive logistic regression for an integration of a prior model and an online classifier learned on one feature view, and then used to update the weak classifiers built on the other feature view. The proposed algorithm has a good ability to recover from drifting by incorporating prior knowledge of the object while being adaptive to appearance changes by effectively combining the complementary strengths of different feature views. Experimental results on a series of challenging video sequences demonstrate the superior performance of our algorithm compared to state-of-the-art tracking algorithms.     

Learning group-based dictionaries for discriminative image representation

Dictionary learning is a critical issue for achieving discriminative image representation in many computer vision tasks such as object detection and image classification. In this paper, a new algorithm is developed for learning discriminative group-based dictionaries, where the inter-concept (category) visual correlations are leveraged to enhance both the reconstruction quality and the discrimination power of the group-based discriminative dictionaries. A visual concept network is first constructed for determining the groups of visually similar object classes and image concepts automatically. For each group of such visually similar object classes and image concepts, a group-based dictionary is learned for achieving discriminative image representation. A structural learning approach is developed to take advantage of our group-based discriminative dictionaries for classifier training and image classification. The effectiveness and the discrimination power of our group-based discriminative dictionaries have been evaluated on multiple popular visual benchmarks.     

Monocular human motion tracking with discriminative sparse representation

In this work, we address the problem of monocular tracking the human motion based on the discriminative sparse representation. The proposed method jointly trains the dictionary and the discriminative linear classifier to separate the human being from the background. We show that using the online dictionary learning, the tracking algorithm can adapt the variation of human appearance and background environment. We compared the proposed method with four state-of-the-art tracking algorithms on eight benchmark video clips (Faceocc, Sylv, David, Singer, Girl, Ballet, OneLeaveShopReenter2cor, and ThreePastShop2cor). Qualitative and quantitative experimental validation results are discussed at length. The proposed algorithm for human tracking achieves superior tracking results, and a Matlab run time on a standard desktop machine of four frames per second.     

Mining Mid-level Visual Patterns with Deep CNN Activations

The purpose of mid-level visual element discovery is to find clusters of image patches that are representative of, and which discriminate between, the contents of the relevant images. Here we propose a pattern-mining approach to the problem of identifying mid-level elements within images, motivated by the observation that such techniques have been very effective, and efficient, in achieving similar goals when applied to other data types. We show that Convolutional Neural Network (CNN) activations extracted from image patches typical possess two appealing properties that enable seamless integration with pattern mining techniques. The marriage between CNN activations and a pattern mining technique leads to fast and effective discovery of representative and discriminative patterns from a huge number of image patches, from which mid-level elements are retrieved. Given the patterns and retrieved mid-level visual elements, we propose two methods to generate image feature representations. The first encoding method uses the patterns as codewords in a dictionary in a manner similar to the Bag-of-Visual-Words model. We thus label this a Bag-of-Patterns representation. The second relies on mid-level visual elements to construct a Bag-of-Elements representation. We evaluate the two encoding methods on object and scene classification tasks, and demonstrate that our approach outperforms or matches the performance of the state-of-the-arts on these tasks.     

正例投票下的L1目标跟踪算法

目的 传统的L1稀疏表示目标跟踪,是将所有候选目标表示为字典模板的线性组合,只考虑了字典模板的整体信息,没有分析目标的局部结构。针对该方法在背景杂乱时容易出现跟踪漂移的问题,提出一种基于正例投票的目标跟踪算法。方法 本文将目标表示成图像块粒子的组合,考虑目标的局部结构。在粒子滤波框架内,构建图像块粒子置信函数和相似性函数,提取正例图像块。最终通过正例权重投票估计跟踪目标的最佳位置。结果 在14组公测视频序列上进行跟踪实验,与多种优秀的目标跟踪算法相比,本文跟踪算法在目标受到背景杂乱、遮挡、光照变化等复杂环境干扰下最为稳定,重叠率达到了0.7,且取得了最低的平均跟踪误差5.90,反映了本文算法的可靠性和有效性。结论 本文正例投票下的L1目标跟踪算法,与经典方法相比,能够解决遮挡、光照变化和快速运动等问题的同时,稳定可靠地实现背景杂乱序列的鲁棒跟踪。     

Visual object tracking based on discriminant DCT features

In this paper, a visual object tracking method is proposed based on sparse 2-dimensional discrete cosine transform (2D DCT) coefficients as discriminative features. To select the discriminative DCT coefficients, we give two propositions. The propositions select the features based on estimated mean of feature distributions in each frame. Some intermediate tracking instances are obtained by (a) computing feature similarity using kernel, (b) finding the maximum classifier score computed using ratio classifier, and (c) combinations of both. Another intermediate tracking instance is obtained using incremental subspace learning method. The final tracked instance amongst the intermediate instances are selected by using a discriminative linear classifier learned in each frame. The linear classifier is updated in each frame using some of the intermediate tracked instances. The proposed method has a better tracking performance as compared to state-of-the-art video trackers in a dataset of 50 challenging video sequences.     

一种新的背景模型建立及更新方法*

提出一种新的建立背景模型和更新背景的方法。首先使用一种新颖的方法建立初始背景,然后对每一输入帧使用背景差分法获得前景,并利用分类器将前景分类为运动目标、静止目标、虚假目标、噪声,最后在背景更新阶段采用基于前景目标的背景更新方法。实验结果表明,该方法能建立可靠初始背景,并能有效地解决背景更新“死锁”问题,增强背景模型的鲁棒性。     

Real-time object tracking via compressive feature selection

Recently, compressive tracking (CT) has been widely proposed for its efficiency, accuracy and robustness on many challenging sequences. Its appearance model employs non-adaptive random projections that preserve the structure of the image feature space. A very sparse measurement matrix is used to extract features by multiplying it with the feature vector of the image patch. An adaptive Bayes classifier is trained using both positive samples and negative samples to separate the target from background. On the CT framework, however, some features used for classification have weak discriminative abilities, which reduces the accuracy of the strong classifier. In this paper, we present an online compressive feature selection algorithm(CFS) based on the CT framework. It selects the features which have the largest margin when using them to classify positive samples and negative samples. For features that are not selected, we define a random learning rate to update them slowly. It makes those weak classifiers preserve more target information, which relieves the drift when the appearance of the target changes heavily. Therefore, the classifier trained with those discriminative features couples its score in many challenging sequences, which leads to a more robust tracker. Numerous experiments show that our tracker could achieve superior result beyond many state-of-the-art trackers.     

一种结合显著性检测与词袋模型的目标识别方法

针对词袋模型易受到无关的背景视觉噪音干扰的问题,提出了一种结合显著性检测与词袋模型的目标识别方法。首先,联合基于图论的视觉显著性算法与一种全分辨率视觉显著性算法,自适应地从原始图像中获取感兴趣区域。两种视觉显著性算法的联合可以提高获取的前景目标的完整性。然后,使用尺度不变特征变换描述子从感兴趣区域中提取特征向量,并通过密度峰值聚类算法对特征向量进行聚类,生成视觉字典直方图。最后,利用支持向量机对目标进行识别。在PASCAL VOC 2007和MSRC-21数据库上的实验结果表明,该方法相比同类方法可以有效地提高目标识别性能。