Overview of contextual tracking approaches in information fusion

Proceedings of: Geospatial InfoFusion III. 2-3 May 2013 Baltimore, Maryland, United States.Many information fusion solutions work well in the intended scenarios; but the applications, supporting data, and capabilities change over varying contexts. One example is weather data for electro-optical target trackers of which standards have evolved over decades. The operating conditions of: technology changes, sensor/target variations, and the contextual environment can inhibit performance if not included in the initial systems design. In this paper, we seek to define and categorize different types of contextual information. We describe five contextual information categories that support target tracking: (1) domain knowledge from a user to aid the information fusion process through selection, cueing, and analysis, (2) environment-to-hardware processing for sensor management, (3) known distribution of entities for situation/threat assessment, (4) historical traffic behavior for situation awareness patterns of life (POL), and (5) road information for target tracking and identification. Appropriate characterization and representation of contextual information is needed for future high-level information fusion systems design to take advantage of the large data content available for a priori knowledge target tracking algorithm construction, implementation, and application.Publicad

Kernel Cross-Correlator

Cross-correlator plays a significant role in many visual perception tasks, such as object detection and tracking. Beyond the linear cross-correlator, this paper proposes a kernel cross-correlator (KCC) that breaks traditional limitations. First, by introducing the kernel trick, the KCC extends the linear cross-correlation to non-linear space, which is more robust to signal noises and distortions. Second, the connection to the existing works shows that KCC provides a unified solution for correlation filters. Third, KCC is applicable to any kernel function and is not limited to circulant structure on training data, thus it is able to predict affine transformations with customized properties. Last, by leveraging the fast Fourier transform (FFT), KCC eliminates direct calculation of kernel vectors, thus achieves better performance yet still with a reasonable computational cost. Comprehensive experiments on visual tracking and human activity recognition using wearable devices demonstrate its robustness, flexibility, and efficiency. The source codes of both experiments are released at https://github.com/wang-chen/KCCComment: The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18

Online Learning Discriminative Dictionary with Label Information for Robust Object Tracking

A supervised approach to online-learn a structured sparse and discriminative representation for object tracking is presented. Label information from training data is incorporated into the dictionary learning process to construct a robust and discriminative dictionary. This is accomplished by adding an ideal-code regularization term and classification error term to the total objective function. By minimizing the total objective function, we learn the high quality dictionary and optimal linear multiclassifier jointly using iterative reweighed least squares algorithm. Combined with robust sparse coding, the learned classifier is employed directly to separate the object from background. As the tracking continues, the proposed algorithm alternates between robust sparse coding and dictionary updating. Experimental evaluations on the challenging sequences show that the proposed algorithm performs favorably against state-of-the-art methods in terms of effectiveness, accuracy, and robustness

Robust visual tracking using feature selection

Visual tracking has become a very important component in computer vision, but achieving a robust, reliable and real time tracking remains a real challenge.In order to improve the actual state-of-the-art, we choose to study and improve one of the most performing adaptive tracker by detection. We selected Struck [27] for this quality performance and his low computational cost that makes it real time. Inspired by the great successes of binary keypoint descriptors, we choose to apply binary description to a patch. We propose to use Multi-Block Local Binary Pattern (MB-LBP), based on its great success in face detection and description. In this work we present a technique for selecting the best features for tracking. In combination with the feature selection we propose a technique to take into account contextual information in order to increase the robustness of the tracker. We propose a solution to add scale adaptation to the algorithm, and suggest to transpose this technique to add rotation adaptation. Experimentally we validate these techniques showing that we outperform the state-of-art racking algorithms. To do that we use a benchmarking tool using 51 videos and compare our algorithm to 29 algorithms

Visual Tracking Algorithms using Different Object Representation Schemes

Visual tracking, being one of the fundamental, most important and challenging areas in computer vision, has attracted much attention in the research community during the past decade due to its broad range of real-life applications. Even after three decades of research, it still remains a challenging problem in view of the complexities involved in the target searching due to intrinsic and extrinsic appearance variations of the object. The existing trackers fail to track the object when there are considerable amount of object appearance variations and when the object undergoes severe occlusion, scale change, out-of-plane rotation, motion blur, fast motion, in-plane rotation, out-of-view and illumination variation either individually or simultaneously. In order to have a reliable and improved tracking performance, the appearance variations should be handled carefully such that the appearance model should adapt to the intrinsic appearance variations and be robust enough for extrinsic appearance variations. The objective of this thesis is to develop visual object tracking algorithms by addressing the deficiencies of the existing algorithms to enhance the tracking performance by investigating the use of different object representation schemes to model the object appearance and then devising mechanisms to update the observation models. A tracking algorithm based on the global appearance model using robust coding and its collaboration with a local model is proposed. The global PCA subspace is used to model the global appearance of the object, and the optimum PCA basis coefficients and the global weight matrix are estimated by developing an iteratively reweighted robust coding (IRRC) technique. This global model is collaborated with the local model to exploit their individual merits. Global and local robust coding distances are introduced to find the candidate sample having similar appearance as that of the reconstructed sample from the subspace, and these distances are used to define the observation likelihood. A robust occlusion map generation scheme and a mechanism to update both the global and local observation models are developed. Quantitative and qualitative performance evaluations on OTB-50 and VOT2016, two popular benchmark datasets, demonstrate that the proposed algorithm with histogram of oriented gradient (HOG) features generally performs better than the state-of-the-art methods considered do. In spite of its good performance, there is a need to improve the tracking performance in some of the challenging attributes of OTB-50 and VOT2016. A second tracking algorithm is developed to provide an improved performance in situations for the above mentioned challenging attributes. The algorithms is designed based on a structural local 2DDCT sparse appearance model and an occlusion handling mechanism. In a structural local 2DDCT sparse appearance model, the energy compaction property of the transform is exploited to reduce the size of the dictionary as well as that of the candidate samples in the object representation so that the computational cost of the l_1-minimization used could be reduced. This strategy is in contrast to the existing models that use raw pixels. A holistic image reconstruction procedure is presented from the overlapped local patches that are obtained from the dictionary and the sparse codes, and then the reconstructed holistic image is used for robust occlusion detection and occlusion map generation. The occlusion map thus obtained is used for developing a novel observation model update mechanism to avoid the model degradation. A patch occlusion ratio is employed in the calculation of the confidence score to improve the tracking performance. Quantitative and qualitative performance evaluations on the two above mentioned benchmark datasets demonstrate that this second proposed tracking algorithm generally performs better than several state-of-the-art methods and the first proposed tracking method do. Despite the improved performance of this second proposed tracking algorithm, there are still some challenging attributes of OTB-50 and of VOT2016 for which the performance needs to be improved. Finally, a third tracking algorithm is proposed by developing a scheme for collaboration between the discriminative and generative appearance models. The discriminative model is explored to estimate the position of the target and a new generative model is used to find the remaining affine parameters of the target. In the generative model, robust coding is extended to two dimensions and employed in the bilateral two dimensional PCA (2DPCA) reconstruction procedure to handle the non-Gaussian or non-Laplacian residuals by developing an IRRC technique. A 2D robust coding distance is introduced to differentiate the candidate sample from the one reconstructed from the subspace and used to compute the observation likelihood in the generative model. A method of generating a robust occlusion map from the weights obtained during the IRRC technique and a novel update mechanism of the observation model for both the kernelized correlation filters and the bilateral 2DPCA subspace are developed. Quantitative and qualitative performance evaluations on the two datasets demonstrate that this algorithm with HOG features generally outperforms the state-of-the-art methods and the other two proposed algorithms for most of the challenging attributes

Advances and Applications of Dezert-Smarandache Theory (DSmT) for Information Fusion (Collected Works), Vol. 4

The fourth volume on Advances and Applications of Dezert-Smarandache Theory (DSmT) for information fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics. The contributions (see List of Articles published in this book, at the end of the volume) have been published or presented after disseminating the third volume (2009, http://fs.unm.edu/DSmT-book3.pdf) in international conferences, seminars, workshops and journals. First Part of this book presents the theoretical advancement of DSmT, dealing with Belief functions, conditioning and deconditioning, Analytic Hierarchy Process, Decision Making, Multi-Criteria, evidence theory, combination rule, evidence distance, conflicting belief, sources of evidences with different importance and reliabilities, importance of sources, pignistic probability transformation, Qualitative reasoning under uncertainty, Imprecise belief structures, 2-Tuple linguistic label, Electre Tri Method, hierarchical proportional redistribution, basic belief assignment, subjective probability measure, Smarandache codification, neutrosophic logic, Evidence theory, outranking methods, Dempster-Shafer Theory, Bayes fusion rule, frequentist probability, mean square error, controlling factor, optimal assignment solution, data association, Transferable Belief Model, and others. More applications of DSmT have emerged in the past years since the apparition of the third book of DSmT 2009. Subsequently, the second part of this volume is about applications of DSmT in correlation with Electronic Support Measures, belief function, sensor networks, Ground Moving Target and Multiple target tracking, Vehicle-Born Improvised Explosive Device, Belief Interacting Multiple Model filter, seismic and acoustic sensor, Support Vector Machines, Alarm classification, ability of human visual system, Uncertainty Representation and Reasoning Evaluation Framework, Threat Assessment, Handwritten Signature Verification, Automatic Aircraft Recognition, Dynamic Data-Driven Application System, adjustment of secure communication trust analysis, and so on. Finally, the third part presents a List of References related with DSmT published or presented along the years since its inception in 2004, chronologically ordered