Object tracking based on online representative sample selection via non-negative least square

Ou, Weihua; Yuan, Di; Li, Qiao; Cao, Yanan

doi:10.1007/s11042-017-4672-3

Cited by 29 publications

(17 citation statements)

References 37 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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“…Li et al proposed a target aware deep tracking framework integrated with the Siamese CNN and target aware features [22]. Liu proposed a tracker that performed a prediction using a given threshold by providing a template update method as a score function between a candidate group and a template [23,24]. This tracker had an effective observation module, which could deal with occasional large appearance variation or severe occlusions.…”

Section: Related Workmentioning

confidence: 99%

Global Motion-Aware Robust Visual Object Tracking for Electro Optical Targeting Systems

Kim

Lukežič

Lee

et al. 2020

Sensors

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Although recently developed trackers have shown excellent performance even when tracking fast moving and shape changing objects with variable scale and orientation, the trackers for the electro-optical targeting systems (EOTS) still suffer from abrupt scene changes due to frequent and fast camera motions by pan-tilt motor control or dynamic distortions in field environments. Conventional context aware (CA) and deep learning based trackers have been studied to tackle these problems, but they have the drawbacks of not fully overcoming the problems and dealing with their computational burden. In this paper, a global motion aware method is proposed to address the fast camera motion issue. The proposed method consists of two modules: (i) a motion detection module, which is based on the change in image entropy value, and (ii) a background tracking module, used to track a set of features in consecutive images to find correspondences between them and estimate global camera movement. A series of experiments is conducted on thermal infrared images, and the results show that the proposed method can significantly improve the robustness of all trackers with a minimal computational overhead. We show that the proposed method can be easily integrated into any visual tracking framework and can be applied to improve the performance of EOTS applications.

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“…[13][14][15] The traditional particle filter algorithm implements a recursive Bayesian framework by using the non-parametric Monte Carlo sampling method, which can effectively track the target object in most scenes. Others improved trackers 17,18 to have more precision and robustness than the traditional particle filter-based trackers. The particle filter can predict the target movement, and these scattered particles are benefit for target fast movement or partial occlusions; it provided a robust tracking framework.…”

Section: Particle Filter Trackermentioning

confidence: 99%

“…Li et al 14 proposed a visual object tracking method based on adaptive background modeling to improve the robustness of the particle filter framework. Others improved trackers 17,18 to have more precision and robustness than the traditional particle filter-based trackers. The main drawbacks of PF tracking are that its localizing precision is rougher than CF.…”

Section: Particle Filter Trackermentioning

confidence: 99%

Collaborating visual tracker based on particle filter and correlation filter

Wang

Han

et al. 2018

Concurrency and Computation

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Correlation filter (CF)-based tracking algorithms is most popular in recent years due to its high accuracy and impressive speed. However, it has some intrinsically drawbacks such as margin suppression, sensitive for disturbance, and partial occlusions. Contrasted with CF drawbacks, the advantages of particle filter (PF) tracking algorithm include robustness, motion prediction, and wide detection range. Therefore, it can amend some CF tracker drawbacks. On the other hand, the HOG feature is widely used in CF tracker because it can detect the target precision position.However, this kind of feature is rotation-variation, which is invalid for rotation transformation target. On the contrary, the tracker precision merely based on colour feature is rough, but colour feature is rotation invariation and is effective for rotating target; therefore, these two features are complementary. In this paper, we integrate both trackers (CF and PF) to learn the HOG and colour feature, respectively, experiments demonstrate this tracking algorithm is more robust, and the tracking precision is more accurate. This algorithm is integrated with some classic CF trackers (KCF, SAMF, and MOSSE) framework and benchmark them against their baseline. On the OTB2015 benchmark datasets, experiment result demonstrates OPE performance grades have improved from about 1% to 12%; SRE Performance grades have improved from about 1.3% to 5.8%.

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Object tracking based on online representative sample selection via non-negative least square

Cited by 29 publications

References 37 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

Global Motion-Aware Robust Visual Object Tracking for Electro Optical Targeting Systems

Collaborating visual tracker based on particle filter and correlation filter

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