相关论文
2018 - Machine Vision and Applications
Visual tracking of resident space objects via an RFS-based multi-Bernoulli track-before-detect method

In this paper, we propose a fast and reliable track-before-detect approach to simultaneously detect, track, and identify an unknown and variable number of resident space objects (RSOs) without any prior information and any explicit detection, which leads to better space domain awareness. Specifically, we use the point spread function concept to propose a separable likelihood function as the observation model in the random finite set-based multi-Bernoulli filtering framework. This framework clearly distinguishes RSOs from any counterfeit objects and detects and tracks them immediately after their respective appearance in background cluttered telescope imagery data. The extensive experimental results on the TAOS dataset demonstrate the robustness of the proposed method in detecting and tracking RSOs with the average optimal subpattern assignment localization error less than 2 pixels in image sequences with the signal to noise ratio as low as 9 dB and under the conditions of varying illumination and occlusion.

2011
Relating proof standards and abstract argumentation

The basic idea of argumentation is to construct arguments in favour of and against a certain statement, selecting the acceptable arguments, and in the end determining which statements hold. To explain how arguments defend their position, they can be structured by their used knowledge and rules. Several approaches to structured argumentation have been developed and subsequently related through a single framework called ASPIC. Although the theoretical relations between these approaches have thus been clarified, there have been significant developments in structured argumentation that deviate from this approach. Carneades is a recently proposed formalism for structured argumentation with varying proof standards, inspired by legal reasoning but more generally applicable. Its distinctive feature is that each statement can be given its own proof standard, which is claimed to allow a more natural account of reasoning under burden of proof than existing formalisms for structured argumentation, in which proof standards are defined globally. However, its relation with Dung’s seminal abstract approach to argumentation is still an open question. In this thesis the two formalisms are formally related by translating Carneades into ASPIC. Since ASPIC is defined to generate Dung-style abstract argumentation frameworks, this in effect translates Carneades graphs into abstract argumentation frameworks. For this translation, we prove a formal correspondence and show that certain rationality postulates hold. It is furthermore proven that Carneades always induces a unique Dung extension, which is the same in all of Dung’s semantics, allowing us to generalise Carneades to cycle-containing structures.

2018 - 2018 24th International Conference on Pattern Recognition (ICPR)
Learning Collaborative Model for Visual Tracking

This paper proposes a robust visual tracking method by designing a collaborative model. The collaborative model employs a two-stage tracker and a HOG-based detector, which exploits both holistic and local information of the target. The two-stage tracker learns a linear classifier from the patches of original images and the HOG-based detector trains a linear discriminant analysis classifier with the object exemplar. Finally, a result decision making strategy is developed by considering both the original template and the appearance variations, making the tracker and the detector collaborate with each other. The proposed method has been evaluated on OTB-50, OTB-100 and Temple-Color datasets, and results demonstrate that the proposed method is able to effectively address the challenging cases such as scale variation and out-of-view and gets better performance than the state-of-the-art trackers.

2019 - Symmetry
Low-Rank Multi-Channel Features for Robust Visual Object Tracking

Kernel correlation filters (KCF) demonstrate significant potential in visual object tracking by employing robust descriptors. Proper selection of color and texture features can provide robustness against appearance variations. However, the use of multiple descriptors would lead to a considerable feature dimension. In this paper, we propose a novel low-rank descriptor, that provides better precision and success rate in comparison to state-of-the-art trackers. We accomplished this by concatenating the magnitude component of the Overlapped Multi-oriented Tri-scale Local Binary Pattern (OMTLBP), Robustness-Driven Hybrid Descriptor (RDHD), Histogram of Oriented Gradients (HoG), and Color Naming (CN) features. We reduced the rank of our proposed multi-channel feature to diminish the computational complexity. We formulated the Support Vector Machine (SVM) model by utilizing the circulant matrix of our proposed feature vector in the kernel correlation filter. The use of discrete Fourier transform in the iterative learning of SVM reduced the computational complexity of our proposed visual tracking algorithm. Extensive experimental results on Visual Tracker Benchmark dataset show better accuracy in comparison to other state-of-the-art trackers.

2017 - Image Vis. Comput.
Learning spatially regularized similarity for robust visual tracking

Matching visual appearances of the target object over consecutive frames is a critical step in visual tracking. The accuracy performance of a practical tracking system highly depends on the similarity metric used for visual matching. Recent attempts to integrate discriminative metric learned by sequential visual data (instead of a predefined metric) in visual tracking have demonstrated more robust and accurate results. However, a global similarity metric is often suboptimal for visual matching when the target object experiences large appearance variation or occlusion. To address this issue, we propose in this paper a spatially weighted similarity fusion (SWSF) method for robust visual tracking. In our SWSF, a part-based model is employed as the object representation, and the local similarity metric and spatially regularized weights are jointly learned in a coherent process, such that the total matching accuracy between visual target and candidates can be effectively enhanced. Empirically, we evaluate our proposed tracker on various challenging sequences against several state-of-the-art methods, and the results demonstrate that our method can achieve competitive or better tracking performance in various challenging tracking scenarios. A spatially weighted similarity fusion scheme for more robust visual trackingLocal similarity metric and its weights are jointly learned in a coherent online process.Evaluations show the robustness to partial occlusion with high matching accuracy.

2018 - 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)
Robust Visual Tracking via Hierarchical Convolutional Features-Based Sparse Learning

In recent years, convolutional features have significantly advanced the Discriminative Correlation Filter (DCF) based trackers. In contrast to hand-crafted ones, features extracted from a CNN retain high spatial resolution while preserving semantic information. The improvements come at the risk of reduction in speed and over-fitting caused by the insufficiency of training data for tracking. In this paper, a novel Hierarchical Convolutional Features and Sparse learning based Tracker (HCFST) is proposed. We effectively tackle the issues of computational bottlenecks and over-fitting in the DCF formulation via the multi-task sparse learning. First, most of the noisy and irrelevant feature maps are safely removed for robust appearance modeling. Redundant features rejection effectively mitigates the redundancy among features from hierarchical layers of CNNs. Then a sparser updating scheme is further presented for conditional model update. Extensive experiments are performed on various challenging sequences from OTB50 and OTB100 datasets. The proposed HCFST performs favorably against state-of-the-art methods.

2018 - IEEE Access
Adaptive Framework for Robust Visual Tracking

Visual tracking is a difficult and challenging problem, for numerous reasons such as small object size, pose angle variations, occlusion, and camera motion. Object tracking has many real-world applications such as surveillance systems, moving organs in medical imaging, and robotics. Traditional tracking methods lack a recovery mechanism that can be used in situations when the tracked objects drift away from ground truth. In this paper, we propose a novel framework for tracking moving objects based on a composite framework and a reporter mechanism. The composite framework tracks moving objects using different trackers and produces pairs of forward/backward tracklets. A robustness score is then calculated for each tracker using its forward/backward tracklet pair to find the most reliable moving object trajectory. The reporter serves as the recovery mechanism to correct the moving object trajectory when the robustness score is very low, mainly using a combination of particle filter and template matching. The proposed framework can handle partial and heavy occlusions; moreover, the structure of the framework enables integration of other user-specific trackers. Extensive experiments on recent benchmarks show that the proposed framework outperforms other current state-of-the-art trackers due to its powerful trajectory analysis and recovery mechanism; the framework improved the area under the curve from 68% to 70.8% on OTB-100 benchmark.

2010 - 2010 20th International Conference on Pattern Recognition
Receding Horizon Estimation for Hybrid Particle Filters and Application for Robust Visual Tracking

The receding horizon estimation is applied to design robust visual trackers. Most recent data within the fixed size of windows is receding, and is processed to obtain an estimate of the object state at the current time. In visual tracking such a scheme improves filter accuracy by avoiding accumulated approximation errors. A newly derived unscented Kalman filter (UKF) based on the receding horizon strategy is proposed for determining the importance density of the hybrid particle filter. The importance density derived by the receding horizon-based UKF (RHUKF) provides significantly improved accuracy and performance consistency compared to the unscented particle filter (UPF). Visual tracking examples are subsequently tested to demonstrate the advantages of the filter.

2012 - Econometrics: Single Equation Models eJournal
Perspectives on Spatial Econometrics: Linear Smoothing with Structured Models

Though standard spatial econometric models may be useful for specification testing, they rely heavily on a parametric structure that is highly sensitive to model misspecification. The commonly used spatial AR model is a form of spatial smoothing with a structure that closely resembles a semiparametric model. Nonparametric and semiparametric models are generally a preferable approach for more descriptive spatial analysis. Estimated population density functions illustrate the differences between the spatial AR model and nonparametric approaches to data smoothing. A series of Monte Carlo experiments demonstrates that nonparametric predicted values and marginal effect estimates are much more accurate then spatial AR models when the contiguity matrix is misspecified.

2017 - Measurement
Robust visual tracking framework in the presence of blurring by arbitrating appearance- and feature-based detection

Abstract This paper proposes a new visual tracking framework and demonstrates its merits via mobile robot experiments. An image sequence from the vision system of a mobile robot is not static when a mobile robot is moving, since slipping and vibration occur. These problems cause image blurring. Therefore, in this paper, we address the problem of robust object tracking under blurring and introduce a novel robust visual tracking framework based on the arbitration of the AdaBoost-based detection method and the appearance-based detection method to overcome the blurring problem. The proposed framework consists of three parts: (1) distortion error compensation and feature extraction using the Modified Discrete Gaussian–Hermite Moment (MDGHM) and fuzzy-based distortion error compensation, (2) object detection using arbitration of appearance- and feature-based object detection, and (3) object tracking using a Finite Impulse Response (FIR) filter. To demonstrate the performance of the proposed framework, mobile robot visual tracking experiments are carried out. The results show that the proposed framework is more robust against blurring than the conventional feature- and appearance-based methods.

2018 - IEEE Transactions on Cybernetics
Unified Graph-Based Multicue Feature Fusion for Robust Visual Tracking

Visual tracking is a complex problem due to unconstrained appearance variations and a dynamic environment. The extraction of complementary information from the object environment via multiple features and adaption to the target’s appearance variations are the key problems of this paper. To this end, we propose a robust object tracking framework based on the unified graph fusion (UGF) of multicue to adapt to the object’s appearance. The proposed cross-diffusion of sparse and dense features not only suppresses the individual feature deficiencies but also extracts the complementary information from multicue. This iterative process builds robust unified features which are invariant to object deformations, fast motion, and occlusion. Robustness of the unified feature also enables the random forest classifier to precisely distinguish the foreground from the background, adding resilience to background clutter. In addition, we present a novel kernel-based adaptation strategy using outlier detection and a transductive reliability metric. The adaptation strategy updates the appearance model to accommodate variations in scale, illumination, and rotation. Both qualitative and quantitative analyses on benchmark video sequences from OTB-50, OTB-100, VOT2017/18, and UAV123 show that the proposed UGF tracker performs favorably against 18 other state-of-the-art trackers under various object tracking challenges.

2015 - IEEE Transactions on Neural Networks and Learning Systems
Robust Exemplar Extraction Using Structured Sparse Coding

Robust exemplar extraction from the noisy sample set is one of the most important problems in pattern recognition. In this brief, we propose a novel approach for exemplar extraction through structured sparse learning. The new model accounts for not only the reconstruction capability and the sparsity, but also the diversity and robustness. To solve the optimization problem, we adopt the alternating directional method of multiplier technology to design an iterative algorithm. Finally, the effectiveness of the approach is demonstrated by experiments of various examples including traffic sign sequences.

2014 - Pattern Recognit.
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. HighlightsA tracker combines the generative and the discriminative model.Introduce maximum margin projection into object tracking.Transform MMP function to be solved under the Spectral Regression framework.Learning an incremental view-based model of the target with sparse representation.Experiments show our tracker to be more robust and stable than state-of-the-art methods.

2017 - Pattern Recognit.
Robust visual tracking via co-trained Kernelized correlation filters

Abstract Recent advances in visual tracking have witnessed the importance of discriminative classifiers tasked with distinguishing the target from the background. However, a single classifier may fail to cope with complex surrounding environment and large appearance variations of the target. Motivated by multi-view learning, we equip a basic framework to train a pool of discriminative classifiers jointly in a closed-form fashion in this paper. It poses an extra regularization term in ridge regression which interacts with other base models in the ensemble. Through a simple realization of this approach, we show co-trained kernelized correlation filters (COKCF) which consist of two KCF trackers, are able to outperform the KCF tracker by a larger margin and perform favorably against other state-of-the-art trackers on 63 benchmark video sequences.

2014 - IEEE Transactions on Industrial Informatics
Robust Visual Tracking Using Flexible Structured Sparse Representation

In this work, we propose a robust and flexible appearance model based on the structured sparse representation framework. In our method, we model the complex nonlinear appearance manifold and the occlusion as a sparse linear combination of structured union of subspaces in a basis library, which consists of multiple incremental learned target subspaces and a partitioned occlusion template set. In order to enhance the discriminative power of the model, a number of clustered background subspaces are also added into the basis library and updated during tracking. With the Block Orthogonal Matching Pursuit (BOMP) algorithm, we show that the new flexible structured sparse representation based appearance model facilitates the tracking performance compared with the prototype structured sparse representation model and other state of the art tracking algorithms.

2017 - IEEE Transactions on Circuits and Systems for Video Technology
Robust Visual Tracking via Basis Matching

Most existing tracking approaches are based on either the tracking by detection framework or the tracking by matching framework. The former needs to learn a discriminative classifier using positive and negative samples, which will cause tracking drift due to unreliable samples. The latter usually performs tracking by matching local interest points between a target candidate and the tracked target, which is not robust to target appearance changes over time. In this paper, we propose a novel tracking by matching framework for robust tracking based on basis matching rather than point matching. In particular, we learn the target model from target images using a set of Gabor basis functions, which have large responses on the corresponding spatial positions after a max pooling. During tracking, a target candidate is evaluated by computing the responses of the Gabor basis functions on their corresponding spatial positions. The experimental results on a set of challenging sequences validate that the performance of the proposed tracking method outperforms those of several state-of-the-art methods.

2013 - IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Visual Tracking Using Local Sparse Appearance Model and K-Selection

Online learned tracking is widely used for its adaptive ability to handle appearance changes. However, it introduces potential drifting problems due to the accumulation of errors during the self-updating, especially for occluded scenarios. The recent literature demonstrates that appropriate combinations of trackers can help balance the stability and flexibility requirements. We have developed a robust tracking algorithm using a local sparse appearance model (SPT) and K-Selection. A static sparse dictionary and a dynamically updated online dictionary basis distribution are used to model the target appearance. A novel sparse representation-based voting map and a sparse constraint regularized mean shift are proposed to track the object robustly. Besides these contributions, we also introduce a new selection-based dictionary learning algorithm with a locally constrained sparse representation, called K-Selection. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than alternatives reported in the recent literature.

2018 - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Multi-cue Correlation Filters for Robust Visual Tracking

In recent years, many tracking algorithms achieve impressive performance via fusing multiple types of features, however, most of them fail to fully explore the context among the adopted multiple features and the strength of them. In this paper, we propose an efficient multi-cue analysis framework for robust visual tracking. By combining different types of features, our approach constructs multiple experts through Discriminative Correlation Filter (DCF) and each of them tracks the target independently. With the proposed robustness evaluation strategy, the suitable expert is selected for tracking in each frame. Furthermore, the divergence of multiple experts reveals the reliability of the current tracking, which is quantified to update the experts adaptively to keep them from corruption. Through the proposed multi-cue analysis, our tracker with standard DCF and deep features achieves outstanding results on several challenging benchmarks: OTB-2013, OTB-2015, Temple-Color and VOT 2016. On the other hand, when evaluated with only simple hand-crafted features, our method demonstrates comparable performance amongst complex non-realtime trackers, but exhibits much better efficiency, with a speed of 45 FPS on a CPU.

2015 - IEEE Transactions on Cybernetics
Online State-Based Structured SVM Combined With Incremental PCA for Robust Visual Tracking

In this paper, we propose a robust state-based structured support vector machine (SVM) tracking algorithm combined with incremental principal component analysis (PCA). Different from the current structured SVM for tracking, our method directly learns and predicts the object's states and not the 2-D translation transformation during tracking. We define the object's virtual state to combine the state-based structured SVM and incremental PCA. The virtual state is considered as the most confident state of the object in every frame. The incremental PCA is used to update the virtual feature vector corresponding to the virtual state and the principal subspace of the object's feature vectors. In order to improve the accuracy of the prediction, all the feature vectors are projected onto the principal subspace in the learning and prediction process of the state-based structured SVM. Experimental results on several challenging video sequences validate the effectiveness and robustness of our approach.

2012 - 2012 IEEE Conference on Computer Vision and Pattern Recognition
Robust visual tracking via multi-task sparse learning

In this paper, we formulate object tracking in a particle filter framework as a multi-task sparse learning problem, which we denote as Multi-Task Tracking (MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in MTT. By employing popular sparsity-inducing ℓp, q mixed norms (p ∈ {2, ∞} and q = 1), we regularize the representation problem to enforce joint sparsity and learn the particle representations together. As compared to previous methods that handle particles independently, our results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity. Interestingly, we show that the popular L1 tracker [15] is a special case of our MTT formulation (denoted as the L11 tracker) when p = q = 1. The learning problem can be efficiently solved using an Accelerated Proximal Gradient (APG) method that yields a sequence of closed form updates. As such, MTT is computationally attractive. We test our proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that MTT methods consistently outperform state-of-the-art trackers.

Robust Visual Tracking via Structured Multi-Task Sparse Learning

Narendra Ahuja
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Tianzhu Zhang
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Bernard Ghanem
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Si Liu
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N. Ahuja
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Bernard Ghanem
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Si Liu
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Tianzhu Zhang

Abstract:In this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in Multi-Task Tracking (MTT). By employing popular sparsity-inducing $$\ell _{p,q}$$ mixed norms $$(\text{ specifically} p\in \{2,\infty \}$$ and $$q=1),$$ we regularize the representation problem to enforce joint sparsity and learn the particle representations together. As compared to previous methods that handle particles independently, our results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity. Interestingly, we show that the popular $$L_1$$ tracker (Mei and Ling, IEEE Trans Pattern Anal Mach Intel 33(11):2259–2272, 2011) is a special case of our MTT formulation (denoted as the $$L_{11}$$ tracker) when $$p=q=1.$$ Under the MTT framework, some of the tasks (particle representations) are often more closely related and more likely to share common relevant covariates than other tasks. Therefore, we extend the MTT framework to take into account pairwise structural correlations between particles (e.g. spatial smoothness of representation) and denote the novel framework as S-MTT. The problem of learning the regularized sparse representation in MTT and S-MTT can be solved efficiently using an Accelerated Proximal Gradient (APG) method that yields a sequence of closed form updates. As such, S-MTT and MTT are computationally attractive. We test our proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that S-MTT is much better than MTT, and both methods consistently outperform state-of-the-art trackers.

摘要：本文将粒子滤波框架中的目标跟踪问题描述为一个结构化多任务稀疏学习问题，我们称之为结构化多任务跟踪(S-mtt)。由于我们将粒子建模为动态更新的字典模板的线性组合，因此在多任务跟踪(MTT)中，学习每个粒子的表示被视为一项单一任务。通过使用流行的稀疏性诱导$$\ell_p，q}$$混合范数$$(特别是p\in2，inty$$和$$q=1)，我们将表示问题正则化以强制联合稀疏性，并一起学习粒子表示。与以往单独处理粒子的方法相比，我们的结果表明，挖掘粒子之间的相互依赖关系提高了跟踪性能和整体计算复杂度。有趣的是，我们证明了流行的$$L_1$$跟踪器(Mei和Ling，IEEE Transans Pattern Anal Mach Intel 33(11)：2259-2272,2011)是我们的MTT公式的特例(表示为$$L_{11}$tracker)，当$$p=q=1时。$$在MTT框架下，一些任务(粒子表示)通常比其他任务更紧密地相关，并且更有可能共享共同的相关协变量。因此，我们扩展了四甲基偶氮唑蓝框架以考虑粒子之间的两两结构相关性(例如，表示的空间光滑性)，并将新的框架命名为S-四甲基偶氮唑蓝。利用一种产生闭合形式更新序列的加速近邻梯度(APG)方法，可以有效地解决在MTT法和S-MTT法中学习正则化稀疏表示的问题。因此，S-四甲基偶氮唑蓝和四甲基偶氮唑蓝在计算上具有吸引力。我们在涉及严重遮挡、剧烈光照变化和较大姿势变化的挑战序列上测试了我们提出的方法。实验结果表明，S-MTT法比MTT法要好得多，而且这两种方法的性能都一致优于最先进的跟踪器。

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引用
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