The Experts below are selected from a list of 164283 Experts worldwide ranked by ideXlab platform
Yazhe Tang - One of the best experts on this subject based on the ideXlab platform.
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structured sparse representation Appearance Model for robust visual tracking
International Conference on Robotics and Automation, 2011Co-Authors: Tianxiang Bai, Yazhe TangAbstract:We propose a robust visual tracker based on structured sparse representation Appearance Model. The Appearance of tracking target is Modeled as a sparse linear combination of Eigen templates plus a sparse error due to occlusions. We address the structured sparse representation that preferably matches the practical visual tracking problem by taking the contiguous spatial distribution of occlusion into account. The sparsity is achieved by Block Orthogonal Matching Pursuit (BOMP) for solving structured sparse representation problem more efficiently. The Model update scheme, based on incremental Singular Value Decomposition (SVD), guarantees the Eigen templates that are able to capture the variations of target Appearance online. Then the approximation error is adopted to build a probabilistic observation Model that integrates with a stochastic affine motion Model to form a particle filter framework for visual tracking. Thanks to the block structure of sparse representation and BOMP, our proposed tracker demonstrates superiority on both efficiency and robustness improvement in comparison experiments with publicly available benchmark video sequences.
Y F Li - One of the best experts on this subject based on the ideXlab platform.
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robust visual tracking with structured sparse representation Appearance Model
Pattern Recognition, 2012Co-Authors: Y F LiAbstract:In this paper, we present a structured sparse representation Appearance Model for tracking an object in a video system. The mechanism behind our method is to Model the Appearance of an object as a sparse linear combination of structured union of subspaces in a basis library, which consists of a learned Eigen template set and a partitioned occlusion template set. We address this structured sparse representation framework that preferably matches the practical visual tracking problem by taking the contiguous spatial distribution of occlusion into account. To achieve a sparse solution and reduce the computational cost, Block Orthogonal Matching Pursuit (BOMP) is adopted to solve the structured sparse representation problem. Furthermore, aiming to update the Eigen templates over time, the incremental Principal Component Analysis (PCA) based learning scheme is applied to adapt the varying Appearance of the target online. Then we build a probabilistic observation Model based on the approximation error between the recovered image and the observed sample. Finally, this observation Model is integrated with a stochastic affine motion Model to form a particle filter framework for visual tracking. Experiments on some publicly available benchmark video sequences demonstrate the advantages of the proposed algorithm over other state-of-the-art approaches.
Zhongfei Zhang - One of the best experts on this subject based on the ideXlab platform.
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single and multiple object tracking using log euclidean riemannian subspace and block division Appearance Model
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012Co-Authors: Wenhan Luo, Xiaoqin Zhang, Stephen J Maybank, Zhongfei ZhangAbstract:Object Appearance Modeling is crucial for tracking objects, especially in videos captured by nonstationary cameras and for reasoning about occlusions between multiple moving objects. Based on the log-euclidean Riemannian metric on symmetric positive definite matrices, we propose an incremental log-euclidean Riemannian subspace learning algorithm in which covariance matrices of image features are mapped into a vector space with the log-euclidean Riemannian metric. Based on the subspace learning algorithm, we develop a log-euclidean block-division Appearance Model which captures both the global and local spatial layout information about object Appearances. Single object tracking and multi-object tracking with occlusion reasoning are then achieved by particle filtering-based Bayesian state inference. During tracking, incremental updating of the log-euclidean block-division Appearance Model captures changes in object Appearance. For multi-object tracking, the Appearance Models of the objects can be updated even in the presence of occlusions. Experimental results demonstrate that the proposed tracking algorithm obtains more accurate results than six state-of-the-art tracking algorithms.
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robust visual tracking based on an effective Appearance Model
European Conference on Computer Vision, 2008Co-Authors: Zhongfei Zhang, Xiaoqin ZhangAbstract:Most existing Appearance Models for visual tracking usually construct a pixel-based representation of object Appearance so that they are incapable of fully capturing both global and local spatial layout information of object Appearance. In order to address this problem, we propose a novel spatial Log-Euclidean Appearance Model (referred as SLAM) under the recently introduced Log-Euclidean Riemannian metric [23]. SLAM is capable of capturing both the global and local spatial layout information of object Appearance by constructing a block-based Log-Euclidean eigenspace representation. Specifically, the process of learning the proposed SLAM consists of five steps--Appearance block division, online Log-Euclidean eigenspace learning, local spatial weighting, global spatial weighting, and likelihood evaluation. Furthermore, a novel online Log-Euclidean Riemannian subspace learning algorithm (IRSL) [14] is applied to incrementally update the proposed SLAM. Tracking is then led by the Bayesian state inference framework in which a particle filter is used for propagating sample distributions over the time. Theoretic analysis and experimental evaluations demonstrate the promise and effectiveness of the proposed SLAM.
Ming Ronnier Luo - One of the best experts on this subject based on the ideXlab platform.
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zcam a colour Appearance Model based on a high dynamic range uniform colour space
Optics Express, 2021Co-Authors: Muhammad Safdar, Jon Yngve Hardeberg, Ming Ronnier LuoAbstract:A colour Appearance Model based on a uniform colour space is proposed. The proposed colour Appearance Model, ZCAM, comprises of comparatively simple mathematical equations, and plausibly agrees with the psychophysical phenomenon of colour Appearance perception. ZCAM consists of ten colour Appearance attributes including brightness, lightness, colourfulness, chroma, hue angle, hue composition, saturation, vividness, blackness, and whiteness. Despite its relatively simpler mathematical structure, ZCAM performed at least similar to the CIE standard colour Appearance Model CIECAM02 and its revision, CAM16, in predicting a range of reliable experimental data.
Timothy F Cootes - One of the best experts on this subject based on the ideXlab platform.
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comparing variations on the active Appearance Model algorithm
British Machine Vision Conference, 2002Co-Authors: Timothy F Cootes, Panachit KittipanyangamAbstract:The Active Appearance Model (AAM) algorithm has proved to be a successful method for matching statistical Models of Appearance to new images. Since the original algorithm was described there have been a variety of suggested modifications to the basic algorithm, each typically claiming to be in some way superior. We review these algorithms and report the results of experiments comparing their performance. We also investigate the effects of different methods of estimating the update matrix used in the algorithm. We find that careful choice of the latter has at least as much effect as the choice of updating technique.
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a comparative evaluation of active Appearance Model algorithms
British Machine Vision Conference, 1998Co-Authors: Timothy F Cootes, G J Edwards, Christopher J TaylorAbstract:An Active Appearance Model (AAM) allows complex Models of shape and Appearance to be matched to new images rapidly. An AAM contains a statistical Model of the shape and grey-level Appearance of an object of interest The associated search algorithm exploits the locally linear relationship between Model parameter displacements and the residual errors between Model instance and image. This relationship can be learnt during a training phase. To match to an image we measure the current residuals and use the Model to predict changes to the current parameters. The algorithm converges in a few iterations. In this paper we describe variations of the basic algorithm aimed at improving the speed and robustness of search. These include subsampling and using image residuals to drive the shape rather than full Appearance Model. We show examples of search and give the results of experiments comparing the performance of the different algorithms.