An Enhanced Adaptive Coupled-Layer LGTracker++

Xiao, Jingjing; Leonardis, Ale

doi:10.1109/iccvw.2013.24

Cited by 20 publications

(17 citation statements)

References 21 publications

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“…Comparison to the state-of-the-art. The ANT was further compared to several top-performing state-of-the-art trackers [27,25,10,20,9] and baseline trackers [22,1,11,34,13,30] on the VOT2013. The results of the evaluation are presented in Figure 3 as raw A-R plot [26], sequencenormalized and attribute-normalized ranking plots.…”

Section: Vot2013 Benchmark Resultsmentioning

confidence: 99%

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Robust visual tracking using template anchors

Čehovin

Leonardis

Kristan

2016

2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

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Deformable part models exhibit excellent performance in tracking non-rigidly deforming targets, but are usually outperformed by holistic models when the target does not deform or in the presence of uncertain visual data. The reason is that part-based models require estimation of a larger number of parameters compared to holistic models and since the updating process is self-supervised, the errors in parameter estimation are amplified with time, leading to a faster accuracy reduction than in holistic models. On the other hand, the robustness of part-based trackers is generally greater than in holistic trackers. We address the problem of self-supervised estimation of a large number of parameters by introducing controlled graduation in estimation of the free parameters. We propose decomposing the visual model into several sub-models, each describing the target at a different level of detail. The sub-models interact during target localization and, depending on the visual uncertainty, serve for cross-sub-model supervised updating. A new tracker is proposed based on this model which exhibits the qualities of part-based as well as holistic models. The tracker is tested on the highly-challenging VOT2013 and VOT2014 benchmarks, outperforming the state-of-the-art.

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Section: Vot2013 Benchmark Resultsmentioning

confidence: 99%

“…Results overview for VOT2013 and VOT2014 benchmarks in terms of average overlap and number of failures. VOT2013 HT [10] CCMS EDFT [9] FoT [27] IVT [22] LGT++ [30] LGT …”

Section: Resultsmentioning

confidence: 99%

Robust visual tracking using template anchors

Čehovin

Leonardis

Kristan

2016

2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

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“…Note that the ranking of the methods differs from the original publication since two new methods (HMMTxD and MEEM) were added and the relative ranking of the methods changed. The top three performing trackers and their average ranks are HMMTxD (8.77), PLT (9.24), LGTpp [31] The second best performing method on the VOT2013 is the unpublished PLT for which just a short description is available in [30]. PLT is a variation of structural SVM that uses multiple features (color, gradients).…”

Section: Methodsmentioning

confidence: 99%

Online adaptive hidden Markov model for multi-tracker fusion

Vojíř

Matas

Nosková

2016

Computer Vision and Image Understanding

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In this paper, we propose a novel method for visual object tracking called HMMTxD. The method fuses observations from complementary out-of-the box trackers and a detector by utilizing a hidden Markov model whose latent states correspond to a binary vector expressing the failure of individual trackers. The Markov model is trained in an unsupervised way, relying on an online learned detector to provide a source of tracker-independent information for a modified Baum-Welch algorithm that updates the model w.r.t. the partially annotated data.We show the effectiveness of the proposed method on combination of two and three tracking algorithms. The performance of HMMTxD is evaluated on two standard benchmarks (CVPR2013 and VOT) and on a rich collection of 77 publicly available sequences. The HMMTxD outperforms the state-of-the-art, often significantly, on all datasets in almost all criteria.

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“…On the other side of the spectrum of trackers are coupledlayer trackers [5,26,38], composed usually from a lower layer of independently tracked features (tracklets) and a higher layer, modelling object shape, motion, etc. The higher layer manages creation of new features such that they are likely to lay on the object, using a soft segmentation mask.…”

Section: Related Workmentioning

confidence: 99%

Exploring Causal Relationships in Visual Object Tracking

Lebeda

Hadfield

Bowden

2015

2015 IEEE International Conference on Computer Vision (ICCV)

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Causal relationships can often be found in visual object tracking between the motions of the camera and that of the tracked object. This object motion may be an effect of the camera motion, e.g. an unsteady handheld camera. But it may also be the cause, e.g. the cameraman framing the object. In this paper we explore these relationships, and provide statistical tools to detect and quantify them; these are based on transfer entropy and stem from information theory. The relationships are then exploited to make predictions about the object location. The approach is shown to be an excellent measure for describing such relationships. On the VOT2013 dataset the prediction accuracy is increased by 62 % over the best non-causal predictor. We show that the location predictions are robust to camera shake and sudden motion, which is invaluable for any tracking algorithm and demonstrate this by applying causal prediction to two state-of-the-art trackers. Both of them benefit, Struck gaining a 7 % accuracy and 22 % robustness increase on the VTB1.1 benchmark, becoming the new state-of-the-art.

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An Enhanced Adaptive Coupled-Layer LGTracker++

Cited by 20 publications

References 21 publications

Robust visual tracking using template anchors

Robust visual tracking using template anchors

Online adaptive hidden Markov model for multi-tracker fusion

Exploring Causal Relationships in Visual Object Tracking

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