Visual Tracking by Affine Kernel Fitting Using Color and Object Boundary

Leichter, Ido; Lindenbaum, Michael; Rivlin, Ehud

doi:10.1109/iccv.2007.4409104

Cited by 7 publications

(1 citation statement)

References 21 publications

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“…The affine object tracker is also performed in [ 7 ]. A kind of affine kernel-based trackers is presented by combining color-related kernel and boundary-related kernel to improve the tracking accuracy [ 8 ]. A kernel-based tracker is proposed by calculating the Gaussian pyramids of the images and applying mean shift algorithm at each pyramid level for the target [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System

Liu

Wang

Gao

2016

Computational Intelligence and Neuroscience

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This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system. The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape. We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video. The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space. It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter. In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour.

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Section: Introductionmentioning

confidence: 99%