IEEE Trans Pattern Anal Mach Intell - A Geometric Particle Filter for Template-Based Visual Tracking.

Tópicos

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Resumo

Existing approaches to template-based visual tracking, in which the objective is to continuously estimate the spatial transformation parameters of an object template over video frames, have primarily been based on deterministic optimization, which as is well-known can result in convergence to local optima. To overcome this limitation of the deterministic optimization approach, in this paper we present a novel particle filtering approach to template-based visual tracking. We formulate the problem as a particle filtering problem on matrix Lie groups, specifically the Special Linear group SL(3) and the two-dimensional affine group Aff (2). Computational performance and robustness are enhanced through a number of features: (i) Gaussian importance functions on the groups are iteratively constructed via local linearization; (ii) the inverse formulation of the Jacobian calculation is used; (iii) template resizing is performed; and (iv) parent-child particles are developed and used. Extensive experimental results using challenging video sequences demonstrate the enhanced performance and robustness of our particle filtering-based approach to template-based visual tracking. We also show that our approach outperforms several state-of-the-art template-based visual tracking methods via experiments using the publicly available benchmark dataset.

Resumo Limpo

exist approach templatebas visual track object continu estim spatial transform paramet object templat video frame primarili base determinist optim wellknown can result converg local optima overcom limit determinist optim approach paper present novel particl filter approach templatebas visual track formul problem particl filter problem matrix lie group specif special linear group sl twodimension affin group aff comput perform robust enhanc number featur gaussian import function group iter construct via local linear ii invers formul jacobian calcul use iii templat resiz perform iv parentchild particl develop use extens experiment result use challeng video sequenc demonstr enhanc perform robust particl filteringbas approach templatebas visual track also show approach outperform sever stateoftheart templatebas visual track method via experi use public avail benchmark dataset

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