IEEE Trans Image Process - Adaptive Kalman filtering for histogram-based appearance learning in infrared imagery.

Tópicos

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Resumo

Targets of interest in video acquired from imaging infrared sensors often exhibit profound appearance variations due to a variety of factors, including complex target maneuvers, ego-motion of the sensor platform, background clutter, etc., making it difficult to maintain a reliable detection process and track lock over extended time periods. Two key issues in overcoming this problem are how to represent the target and how to learn its appearance online. In this paper, we adopt a recent appearance model that estimates the pixel intensity histograms as well as the distribution of local standard deviations in both the foreground and background regions for robust target representation. Appearance learning is then cast as an adaptive Kalman filtering problem where the process and measurement noise variances are both unknown. We formulate this problem using both covariance matching and, for the first time in a visual tracking application, the recent autocovariance least-squares (ALS) method. Although convergence of the ALS algorithm is guaranteed only for the case of globally wide sense stationary process and measurement noises, we demonstrate for the first time that the technique can often be applied with great effectiveness under the much weaker assumption of piecewise stationarity. The performance advantages of the ALS method relative to the classical covariance matching are illustrated by means of simulated stationary and nonstationary systems. Against real data, our results show that the ALS-based algorithm outperforms the covariance matching as well as the traditional histogram similarity-based methods, achieving sub-pixel tracking accuracy against the well-known AMCOM closure sequences and the recent SENSIAC automatic target recognition dataset.

Resumo Limpo

target interest video acquir imag infrar sensor often exhibit profound appear variat due varieti factor includ complex target maneuv egomot sensor platform background clutter etc make difficult maintain reliabl detect process track lock extend time period two key issu overcom problem repres target learn appear onlin paper adopt recent appear model estim pixel intens histogram well distribut local standard deviat foreground background region robust target represent appear learn cast adapt kalman filter problem process measur nois varianc unknown formul problem use covari match first time visual track applic recent autocovari leastsquar al method although converg al algorithm guarante case global wide sens stationari process measur nois demonstr first time techniqu can often appli great effect much weaker assumpt piecewis stationar perform advantag al method relat classic covari match illustr mean simul stationari nonstationari system real data result show alsbas algorithm outperform covari match well tradit histogram similaritybas method achiev subpixel track accuraci wellknown amcom closur sequenc recent sensiac automat target recognit dataset

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