Robust Visual Tracking And Vehicle Classification Via Sparse Representation Pdf
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On-line visual tracking of a specified target in motion throughout frames of video clips faces challenges in robust identification of the target in the current frame based on the past frames.
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- Robust Visual Object Tracking via Sparse Representation and Reconstruction
- Kernel joint visual tracking and recognition based on structured sparse representation
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Pattern Recognition. ISSN The multi-view sparse representation based visual tracking has attracted increasing attention because the sparse representations of different object features can complement with each other. Since the robustness of different object features is actually not the same in challenging video sequences, it may contain unreliable features the features with low robustness in multi-view sparse representation. In this case, how to highlight the useful information of unreliable features for proper multi-feature fusion has become a tough work. To solve this problem, we propose a multi-view discriminant sparse representation method for robust visual tracking, in which we firstly divide the multi-view observations into different groups, and then estimate the sparse representations of multi-view group projections for calculating the observation likelihood.
Visual object tracking plays an essential role in vision based applications. Most of the previous research has limitations due to the non-discriminated features used or the focus on simple template matching without the consideration of appearance variations. To address these challenges, this paper proposes a new approach for robust visual object tracking via sparse representation and reconstruction, where two main contributions are devoted in terms of object representation and location respectively. And the sparse representation and reconstruction SR 2 are integrated into a Kalman filter framework to form a robust object tracker named as SR 2 KF tracker. The extensive experiments show that the proposed tracker is able to tolerate the appearance variations, background clutter and image deterioration, and outperforms the existing work. Unable to display preview. Download preview PDF.
Bae and K. DOI : Benfold and I. Reid , Stable multi-target tracking in realtime surveillance video , Conference on Computer Vision and Pattern Recognition , pp. Bernardin and R.
Robust Visual Object Tracking via Sparse Representation and Reconstruction
Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Robust Visual Tracking and Vehicle Classification via Sparse Representation Abstract: 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. Then, the candidate with the smallest projection error is taken as the tracking target.
Box , Beijing Volume 40 Issue 7 Jul. Turn off MathJax Article Contents. Journal of Electronics and Information Technology, , 40 7 : PDF KB. In traditional sparse representation based visual tracking, particle sampling is first achieved by particle filter method.
In this paper, we propose an efficient tracking method that is formulated as a multi-task reverse sparse representation problem. The proposed method learns the representation of all tasks jointly using a customized APG method within several iterations. In order to reduce the computational complexity, the proposed tracking algorithm starts from a feature selection scheme that chooses suitable number of features from the object and background in the dynamic environment. Based on the selected feature, multiple templates are constructed with a few candidates. The candidate that corresponds to the highest similarity to the object templates is considered as the final tracking result.
Kernel joint visual tracking and recognition based on structured sparse representation
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: In this paper, we propose a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework. Then, the candidate with the smallest projection error is taken as the tracking target.
It is a challenging issue to deal with kinds of appearance variations in visual tracking. Existing tracking algorithms build appearance models upon target templates. Those models are not robust to significant appearance variations due to factors such as illumination variations, partial occlusions, and scale variation.
По сути, это был самый настоящий шантаж. Он предоставил АНБ выбор: либо рассказать миру о ТРАНСТЕКСТЕ, либо лишиться главного банка данных. Сьюзан в ужасе смотрела на экран.
Коммандер Стратмор обошел систему Сквозь строй. Фонтейн подошел к ней, едва сдерживая гнев. - Это его прерогатива.
Мистер Клушар, очень важно, чтобы вы вспомнили это. - Внезапно Беккер понял, что говорит чересчур громко. Люди на соседних койках приподнялись и внимательно наблюдали за происходящим.