Patch-based visual tracking with online representative sample selection

Ou, Weihua; Li, Donghao; Liu, Bin; Xia, Daoxun; Zeng, Wu

doi:10.1117/1.jei.26.3.033006

Cited by 17 publications

(8 citation statements)

References 51 publications

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“…Hence, it is in extraordinary need of a programmed indicator to relieve the genuine negative effects brought about by the fake news [6]. There are many methodology such as correlation filter based tracking algorithms [7], non-negative least square algorithm [8], Online Representative Sample Selection method [9], regularization framework [10], multiple feature fused model [11] have been introduced.…”

Section: Introductionmentioning

confidence: 99%

A robust technique of fake news detection using Ensemble Voting Classifier and comparison with other classifiers

Mahabub

2020

SN Appl. Sci.

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These days online networking is generally utilized as the wellspring of data as a result of its ease, simple to get to nature. In any case, expending news from online life is a twofold edged sword as a result of the widespread of fake news, i.e., news with purposefully false data. Fake news is a major issue since it affects people just as society substantial. In the internet based life, the data is spread quick and subsequently discovery component ought to almost certainly foresee news quick enough to stop the dispersal of fake news. Consequently, identifying fake news via web-based networking media is a critical and furthermore an in fact testing issue. In this paper, Ensemble Voting Classifier based, an intelligent detection system is proposed to deal with news classification both real and fake tasks. Here, eleven mostly well-known machine-learning algorithms like Naïve Bayes, K-NN, SVM, Random Forest, Artificial Neural Network, Logistic Regression, Gradient Boosting, Ada Boosting, etc. are used for detection. After cross-validation, we used the best three machinelearning algorithms in Ensemble Voting Classifier. The experimental outcomes affirm that the proposed framework can accomplish to about 94.5% outcomes as far as accuracy. The other parameters like ROC score, precision, recall and F1 are also outstanding. The proposed recognition framework can effectively find the most important highlights of the news. These can also be implemented in other classification techniques to detect fake profiles, fake message, etc.

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Section: Introductionmentioning

confidence: 99%

A robust technique of fake news detection using Ensemble Voting Classifier and comparison with other classifiers

Mahabub

2020

SN Appl. Sci.

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“…The literature [21] proposes a patch-based visual tracker that divides the object and the candidate area into several small blocks evenly, and uses the average score of overall small blocks to determine the optimal candidate, which greatly improves under the occlusion circumstances. The literature [22] proposes an online representative sample selection method to construct an effective observation module that can handle occasional large appearance changes or severe occlusion.…”

Section: Related Work 21 Correlation Filtermentioning

confidence: 99%

Long-term target tracking combined with re-detection

Wang

Yang

et al. 2020

Preprint

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The long-term visual tracking undergoes more challenges and is closer to realistic applications than short-term tracking. However, the performances of most existing methods have been limited in the long-term tracking tasks. In this work, we present a reliable yet simple long-term tracking method, which extends the state-of-the-art Learning Adaptive Discriminative Correlation Filters (LADCF) tracking algorithm with a re-detection component based on the SVM model. The LADCF tracking algorithm localizes the target in each frame and the re-detector is able to efficiently re-detect the target in the whole image when the tracking fails. We further introduce a robust confidence degree evaluation criterion that combines the maximum response criterion and the average peak-to correlation energy (APCE) to judge the confidence level of the predicted target. When the confidence degree is generally high, the SVM is updated accordingly. If the confidence drops sharply, the SVM re-detects the target. We perform extensive experiments on the OTB-2015 and UAV123 datasets. The experimental results demonstrate the effectiveness of our algorithm in long-term tracking.

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“…Particle filter based algorithms have been studied in visual object tracking for many years and their variations are still widely used nowadays [2,5,24,25,26]. The traditional particle filter algorithm implements a recursive Bayesian framework by using the nonparametric Monte Carlo sampling method, which can effectively track the target objects in most scenes [1].…”

Section: Particle Filter Based Trackersmentioning

confidence: 99%

Particle filter re-detection for visual tracking via correlation filters

Yuan

et al. 2018

Multimed Tools Appl

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Most of the correlation filter based tracking algorithms can achieve good performance and maintain fast computational speed. However, in some complicated tracking scenes, there is a fatal defect that causes the object to be located inaccurately. In order to address this problem, we propose a particle filter redetection based tracking approach for accurate object localization. During the tracking process, the kernelized correlation filter (KCF) based tracker locates the object by relying on the maximum response value of the response map; when the response map becomes ambiguous, the KCF tracking result becomes unreliable. Our method can provide more candidates by particle resampling to detect the object accordingly. Additionally, we give a new object scale evaluation mechanism, which merely considers the differences between the maximum response values in consecutive frames. Extensive experiments on OTB2013 and OTB2015 datasets demonstrate that the proposed tracker performs favorably in relation to the state-of-the-art methods.

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Patch-based visual tracking with online representative sample selection

Cited by 17 publications

References 51 publications

A robust technique of fake news detection using Ensemble Voting Classifier and comparison with other classifiers

A robust technique of fake news detection using Ensemble Voting Classifier and comparison with other classifiers

Long-term target tracking combined with re-detection

Particle filter re-detection for visual tracking via correlation filters

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