File Name: robust visual tracking and vehicle classification via sparse representation .zip
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. Three approaches for tracking the target image patch are described and compared.
- Kernel joint visual tracking and recognition based on structured sparse representation
- Robust Visual Tracking with Discrimination Dictionary Learning
- Robust Visual Object Tracking via Sparse Representation and Reconstruction
- Canonical Correlation Analysis Based Sparse Representation Model for Robust Visual Tracking
Kernel joint visual tracking and recognition based on structured sparse representation
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. Three approaches for tracking the target image patch are described and compared. These approaches utilize particle filtering and principal component analysis PCA to identify the most likely location of the target in the current frame and a low dimensional subspace representation of the patches of images to be kept as the templates in the dictionary for the identification.
By using a combination of methods and compare the result of each, a new model based is proposed. The goal is to achieve a more robust and accurate tracking of a target throughout the video and continue updating the identification templates to adapt the target changes, such as apparences in lighting, angle, scale and occlusions.
The challenges in tracking are to introduction of the "right" templates into the identification templates in the dictionary and identify the most accurate particle image patch while tracking the target with the right tracking patch scaling. The first approach considered and on which the structure of the visual tracker is based is the "Incremental Learning for Robust Visual Tracking" by D.
Ross et al. This elimination scheme has very limited robustness in tracking, therefore, more selective processes in accepting identification templates in the dictionary are explored and introduced on top of the existing method in comparison and to address the challenges in on-line video tracking. Wang et al. This method is also computationally cheap in comparison to the first approach, and its accuracy is also better than the first approach, but it would sometimes fail to track in some applications.
Mei et al. This approach performs well in comparison to the first and the second approaches in tracking accuracy and robustness, but this approach is extremely computationally expensive.
Three new components are proposed in an effort to mitigate some of the limitations that the three approaches exhibit. One such component is to simply reject the image patches that exhibit too great of difference to the current template dictionary, which resulted in improved tracking robustness.
This method is computationally cheap and easy to implement. Another component introduced is a second set of dictionary that is composed of admitted image patches, which is used for tracking when the image patches appears to be too dissimilar to the dictionary with low dimensional representation.
It is expected that with more well defined and stronger features, it forces the tracking to identify the target. Finally, the third component introduced is the to prevent shrinkage of the target boundary box by weighting the particles drawn with the ratio of area change so that more weight is placed on particles with less arial change.
This increases the likelihood of recovering the target again if tracking loses the target, and instead of shrinking the boundary box, the tracking is biased to staying with the image patch of the same size. The resulting performance of the proposed tracking scheme has not been noticeably improved, part of the reason is because the metrics available to identify a noisy image patch from the good image patches are not always indicative of the noisy-good image patch divide.
Skip to main content. UC Irvine. Email Facebook Twitter. Abstract 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. Thumbnails Document Outline Attachments. Highlight all Match case. Whole words. Toggle Sidebar. Zoom Out.
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Robust Visual Tracking with Discrimination Dictionary Learning
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. In this paper, we propose a robust tracking algorithm with a learnt dictionary to represent target candidates. With the learnt dictionary, a target candidate is represented with a linear combination of dictionary atoms.
Robust Visual Object Tracking via Sparse Representation and Reconstruction
Bae and K. DOI : Benfold and I. Reid , Stable multi-target tracking in realtime surveillance video , Conference on Computer Vision and Pattern Recognition , pp.
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.
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.
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.
Canonical Correlation Analysis Based Sparse Representation Model for Robust Visual Tracking
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.
Box , Beijing Volume 40 Issue 7 Jul. Turn off MathJax Article Contents. Journal of Electronics and Information Technology, , 40 7 :
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