IEEE Trans Image Process - Real-time object tracking via online discriminative feature selection.

Tópicos

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Resumo

Most tracking-by-detection algorithms train discriminative classifiers to separate target objects from their surrounding background. In this setting, noisy samples are likely to be included when they are not properly sampled, thereby causing visual drift. The multiple instance learning (MIL) paradigm has been recently applied to alleviate this problem. However, important prior information of instance labels and the most correct positive instance (i.e., the tracking result in the current frame) can be exploited using a novel formulation much simpler than an MIL approach. In this paper, we show that integrating such prior information into a supervised learning algorithm can handle visual drift more effectively and efficiently than the existing MIL tracker. We present an online discriminative feature selection algorithm that optimizes the objective function in the steepest ascent direction with respect to the positive samples while in the steepest descent direction with respect to the negative ones. Therefore, the trained classifier directly couples its score with the importance of samples, leading to a more robust and efficient tracker. Numerous experimental evaluations with state-of-the-art algorithms on challenging sequences demonstrate the merits of the proposed algorithm.

Resumo Limpo

trackingbydetect algorithm train discrimin classifi separ target object surround background set noisi sampl like includ proper sampl therebi caus visual drift multipl instanc learn mil paradigm recent appli allevi problem howev import prior inform instanc label correct posit instanc ie track result current frame can exploit use novel formul much simpler mil approach paper show integr prior inform supervis learn algorithm can handl visual drift effect effici exist mil tracker present onlin discrimin featur select algorithm optim object function steepest ascent direct respect posit sampl steepest descent direct respect negat one therefor train classifi direct coupl score import sampl lead robust effici tracker numer experiment evalu stateoftheart algorithm challeng sequenc demonstr merit propos algorithm

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