The Template Update Problem

Matthews, Iain; Ishikawa, Takahiro; Baker, Simon

doi:10.5244/c.17.65

Cited by 271 publications

(402 citation statements)

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“…Due to a further noise suppression, much better results are obtained when temporally smoothed values are used within (12). This can be achieved by substituting #8 in rennes1, frame 220 #44 in rennes1, frame 220 the error image pixels e i with a difference between the mean value of the warped feature pixel estimated by (11), and the corresponding reference pixel.…”

Section: The Volatile Feature Support Due To a Robust Rejection Rulementioning

confidence: 99%

“…The technique is related to robust estimation of the warp parameters [10,11], but is more suitable for detecting correct feature supports which often contain statistical outliers. Here we do not consider updating the reference [12,13,11] despite its potential for increasing the tracking flexibility, since it offers less precision while requiring more processing power. The related research also includes the cumulative similarity transform [14] which is suitable only for tracking homogeneous regions, and the probabilistic filtering of the feature position [15,13,11], which has been used for handling temporary total occlusions.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing the Point Feature Tracker by Adaptive Modelling of the Feature Support

Šegvić

Remazeilles

Chaumette

2006

Computer Vision – ECCV 2006

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Abstract. We consider the problem of tracking a given set of point features over large sequences of image frames. A classic procedure for monitoring the tracking quality consists in requiring that the current features nicely warp towards their reference appearances. The procedure recommends focusing on features projected from planar 3D patches (planar features), by enforcing a conservative threshold on the residual of the difference between the warped current feature and the reference. However, in some important contexts, there are many features for which the planarity assumption is only partially satisfied, while the true planar features are not so abundant. This is especially true when the motion of the camera is mainly translational and parallel to the optical axis (such as when driving a car along straight sections of the road), which induces a permanent increase of the apparent feature size. Tracking features containing occluding boundaries then becomes an interesting goal, for which we propose a multi-scale monitoring solution striving to maximize the lifetime of the feature, while also detecting the tracking failures. The devised technique infers the parts of the reference which are not projected from the same 3D surface as the patch which has been consistently tracked until the present moment. The experiments on real sequences taken from cars driving through urban environments show that the technique is effective in increasing the average feature lifetimes, especially in sequences with occlusions and large photometric variations.The presented work has been performed within the french national project Predit Mobivip (Individual Public Vehicles for Mobility in the City), and within the project Robea Bodega. The authors would also like to acknowledge Prof. Axel Pinz for a helpful discussion on this subject.

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Section: The Volatile Feature Support Due To a Robust Rejection Rulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing the Point Feature Tracker by Adaptive Modelling of the Feature Support

Šegvić

Remazeilles

Chaumette

2006

Computer Vision – ECCV 2006

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“…This fact might cause the gradient descent algorithm to converge to a local minimum. Matthews et al (2004) propose the passive drift correction (PDC), which avoids the drifting problem of the simple updating algorithm. Its main idea is to use the first template to correct the drift as an object is tracked in a video sequence.…”

Section: Iteratively Updated Robust Algorithmmentioning

confidence: 99%

A robust template tracking algorithm with weighted active drift correction

Fan

Zhu

et al. 2011

Pattern Recognition Letters

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“…To reduce visual drifts, several algorithms have been developed to facilitate adaptive appearance models in recent years. Matthews et al 33 propose a tracking method with the Lucas-Kanade algorithm by updating the template with the results from the most recent frames and a fixed reference template extracted from the first frame. In contrast to supervised discriminative object tracking, Grabner et al 5 formulate the update problem as a semi-supervised task where the drawn samples are treated as unlabeled data.…”

Section: Adaptive Appearance Modelmentioning

confidence: 99%

An experimental comparison of online object-tracking algorithms

et al. 2011

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This paper reviews and evaluates several state-of-the-art online object tracking algorithms. Notwithstanding decades of efforts, object tracking remains a challenging problem due to factors such as illumination, pose, scale, deformation, motion blur, noise, and occlusion. To account for appearance change, most recent tracking algorithms focus on robust object representations and effective state prediction. In this paper, we analyze the components of each tracking method and identify their key roles in dealing with specific challenges, thereby shedding light on how to choose and design algorithms for different situations. We compare state-of-the-art online tracking methods including the IVT, 1 VRT, 2 FragT, 3 BoostT, 4 SemiT, 5 BeSemiT, 6 L1T, 7 MILT, 8 VTD 9 and TLD 10 algorithms on numerous challenging sequences, and evaluate them with different performance metrics. The qualitative and quantitative comparative results demonstrate the strength and weakness of these algorithms.

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The Template Update Problem

Cited by 271 publications

References 0 publications

Enhancing the Point Feature Tracker by Adaptive Modelling of the Feature Support

Enhancing the Point Feature Tracker by Adaptive Modelling of the Feature Support

A robust template tracking algorithm with weighted active drift correction

An experimental comparison of online object-tracking algorithms

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