IEEE Trans Image Process - Visual tracking with spatio-temporal Dempster-Shafer information fusion.

Tópicos

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{ featur(3375) classif(2383) classifi(1994) }
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Resumo

A key problem in visual tracking is how to effectively combine spatio-temporal visual information from throughout a video to accurately estimate the state of an object. We address this problem by incorporating Dempster-Shafer (DS) information fusion into the tracking approach. To implement this fusion task, the entire image sequence is partitioned into spatially and temporally adjacent subsequences. A support vector machine (SVM) classifier is trained for object/nonobject classification on each of these subsequences, the outputs of which act as separate data sources. To combine the discriminative information from these classifiers, we further present a spatio-temporal weighted DS (STWDS) scheme. In addition, temporally adjacent sources are likely to share discriminative information on object/nonobject classification. To use such information, an adaptive SVM learning scheme is designed to transfer discriminative information across sources. Finally, the corresponding DS belief function of the STWDS scheme is embedded into a Bayesian tracking model. Experimental results on challenging videos demonstrate the effectiveness and robustness of the proposed tracking approach.

Resumo Limpo

key problem visual track effect combin spatiotempor visual inform throughout video accur estim state object address problem incorpor dempstershaf ds inform fusion track approach implement fusion task entir imag sequenc partit spatial tempor adjac subsequ support vector machin svm classifi train objectnonobject classif subsequ output act separ data sourc combin discrimin inform classifi present spatiotempor weight ds stwds scheme addit tempor adjac sourc like share discrimin inform objectnonobject classif use inform adapt svm learn scheme design transfer discrimin inform across sourc final correspond ds belief function stwds scheme embed bayesian track model experiment result challeng video demonstr effect robust propos track approach

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