IEEE Trans Pattern Anal Mach Intell - Robust Object Tracking with Online Multiple Instance Learning.

Tópicos

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Resumo

In this paper, we address the problem of tracking an object in a video given its location in the first frame and no other information. Recently, a class of tracking techniques called "tracking by detection" has been shown to give promising results at real-time speeds. These methods train a discriminative classifier in an online manner to separate the object from the background. This classifier bootstraps itself by using the current tracker state to extract positive and negative examples from the current frame. Slight inaccuracies in the tracker can therefore lead to incorrectly labeled training examples, which degrade the classifier and can cause drift. In this paper, we show that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems and can therefore lead to a more robust tracker with fewer parameter tweaks. We propose a novel online MIL algorithm for object tracking that achieves superior results with real-time performance. We present thorough experimental results (both qualitative and quantitative) on a number of challenging video clips.

Resumo Limpo

paper address problem track object video given locat first frame inform recent class track techniqu call track detect shown give promis result realtim speed method train discrimin classifi onlin manner separ object background classifi bootstrap use current tracker state extract posit negat exampl current frame slight inaccuraci tracker can therefor lead incorrect label train exampl degrad classifi can caus drift paper show use multipl instanc learn mil instead tradit supervis learn avoid problem can therefor lead robust tracker fewer paramet tweak propos novel onlin mil algorithm object track achiev superior result realtim perform present thorough experiment result qualit quantit number challeng video clip

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