IEEE Trans Image Process - Studentized dynamical system for robust object tracking.

Tópicos

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Resumo

This paper describes a studentized dynamical system (SDS) for robust target tracking using a subspace representation. Dynamical systems (DS) provide a powerful framework for the probabilistic modeling of temporal sequences. Visual tracking problems are often cast as a sequential inference problem within the DS framework and a compact way to model the observation distributions (i.e., object appearances) is through probabilistic principal component analysis (PPCA). PPCA is a classic Gaussian based subspace representation method and a popular tool for appearance modeling. Although Gaussian density has theoretically nice properties, resulting in models that are always tractable, they are also severely limited in practical settings. One of the central issues in the use of PPCA for target appearance modeling is that it is very sensitive to outliers. The Gaussian density has a very light tail, while real world data with outliers exhibit heavy tails. Recently, more heavy-tailed distributions (e.g., Student's t-distribution) have been introduced to increase the robustness of the original PPCA. We propose to augment the traditional target tracking DS by adding a set of auxiliary latent variables to adjust the shape of the observation distribution. We show that by carefully choosing the probability density of these auxiliary latent variables, a more robust observation distribution can be obtained with tails heavier than Gaussian. Numerical experiments verify that the proposed SDS has a better capability to handle considerable amount of outlier noise and an improved tracking performance over DS with a Gaussian based observation model.

Resumo Limpo

paper describ student dynam system sds robust target track use subspac represent dynam system ds provid power framework probabilist model tempor sequenc visual track problem often cast sequenti infer problem within ds framework compact way model observ distribut ie object appear probabilist princip compon analysi ppca ppca classic gaussian base subspac represent method popular tool appear model although gaussian densiti theoret nice properti result model alway tractabl also sever limit practic set one central issu use ppca target appear model sensit outlier gaussian densiti light tail real world data outlier exhibit heavi tail recent heavytail distribut eg student tdistribut introduc increas robust origin ppca propos augment tradit target track ds ad set auxiliari latent variabl adjust shape observ distribut show care choos probabl densiti auxiliari latent variabl robust observ distribut can obtain tail heavier gaussian numer experi verifi propos sds better capabl handl consider amount outlier nois improv track perform ds gaussian base observ model

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