Robust visual ℓ2-regularized least squares tracker with Bayes classifier and coding error

Hou, Yueen; Li, Weiguang; Rong, Aiqiong; Lou, Huidong; Quan, Sibo

doi:10.1117/1.jei.22.4.043036

Cited by 4 publications

(4 citation statements)

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“…Recently, sparse representation has been introduced to the tracking task. 3,[19][20][21][22][23] Mei et.al proposed the L1 tracking method. 3 For tracking in their algorithm, a candidate sample can be sparsely represented by a template set or dictionary, and its corresponding likelihood is determined by the reconstruction error with respect to target templates.…”

Section: Motivationmentioning

confidence: 99%

“…Inspired by using generative and discriminative models together to enhance the robustness of the tracker, a structured collaborative representation-based visual tracking algorithm is proposed. 20 Firstly, positive and negative samples are represented by their structured collaborative representation coefficients which obtained by encoding sparse representation with target and background templates, then the structured collaborative representation coefficients are used to train a Bayes classifier which can offer each candidate a classification score. This method is similar to Wang's work, 19 as sparse coefficients are used for object representation, and then a classifier is trained to distinguish target from background.…”

Section: Motivationmentioning

confidence: 99%

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Robust object tracking based on weighted subspace reconstruction error with forward: backward tracking criterion

Zhou

Xie

Zhang

et al. 2015

J. Electron. Imaging

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It is a challenging task to develop an effective and robust object tracking method due to factors such as severe occlusion, background clutters, abrupt motion, illumination variation and so on. In this paper, a novel tracking algorithm based on weighted subspace reconstruction error is proposed. The discriminative weights are defined through minimizing reconstruction error with positive dictionary while maximizing reconstruction error with negative dictionary. Then, confidence map for candidates is computed through subspace reconstruction error. Finally, the location of the target object is estimated by maximizing the decision map which is combined discriminative weights and subspace reconstruction error. Furthermore, the new evaluation method based on forward-backward tracking criterion to verify the robustness of the current tracking performance in updating stage, which can reduce the accumulated error effectively. Experimental results on some challenging video sequences show that the proposed algorithm performs favorably against eleven state-of-the-art methods in terms of accuracy and robustness.Object tracking is one of the most research topics due to its wide range of applications such as behavior analysis, activity recognition, video surveillance, and human-computer interaction. Although it has obtained a significant progress in the past decades, developing an efficient and robust tracking algorithm is still a challenging task due to numerous factors such as illumination variation, partial occlusion, pose change, abrupt motion, background clutter and so on.The main tracking algorithms can be classified into two kinds: generative 1-7 or discriminative methods. [8][9][10][11][12] Generative methods focus on searching for the regions which are the most similar to the tracked targets with minimal reconstruction errors of tracking. Adaptive models including the WSL tracker 13 and IVT method 4 have been proposed to handle appearance variation. Adam et.al 1 used several

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Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Robust object tracking based on weighted subspace reconstruction error with forward: backward tracking criterion

Zhou

Xie

Zhang

et al. 2015

J. Electron. Imaging

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“…The target state in th frame is = arg max ( ( | 1: )) . (12) Here, ( | 1: ) can be obtained by solving the following equations:…”

Section: Object Trackingmentioning

confidence: 99%

“…The algorithm gives full consideration to the sparse representation coefficients and residual error information while locating the target, which improves the robustness of the tracking algorithm. In the framework of structural sparse representation, the sparse coefficients of the candidate targets is classified in [12] by training the Naive Bayes Classifier, strengthening the algorithm's ability to differentiate between the target and its background. But the algorithm fails to extract the features with differentiation; its robustness needs to be further improved.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection Tracking Algorithm Based on Sparse Representation

Lou

Hou

et al. 2015

Mathematical Problems in Engineering

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In order to enhance the robustness of visual tracking algorithm in complex environment, a novel visual tracking algorithm based on multifeature selection and sparse representation is proposed. In the framework of particles filter, particles with low target similarity are first filtered out by a fast algorithm; then, based on the principle of sparsely reconstructing the sample label, the features with high differentiation against the background are involved in the computation so as to reduce the disturbance of occlusions and noises. Finally, candidate targets are linearly reconstructed via sparse representation and the sparse equation is solved by using APG method to obtain the state of the target. Four comparative experiments demonstrate that the proposed algorithm in this paper has effectively improved the robustness of the target tracking algorithm.

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Robust Visual Tracker via Cascade Classifying

Lou

et al. 2018

2018 Chinese Control and Decision Conference (CCDC)

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Robust visual ℓ2-regularized least squares tracker with Bayes classifier and coding error

Cited by 4 publications

References 0 publications

Robust object tracking based on weighted subspace reconstruction error with forward: backward tracking criterion

Robust object tracking based on weighted subspace reconstruction error with forward: backward tracking criterion

Feature Selection Tracking Algorithm Based on Sparse Representation

Robust Visual Tracker via Cascade Classifying

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