IEEE Trans Image Process - Particle filter with a mode tracker for visual tracking across illumination changes.

Tópicos

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Resumo

In this correspondence, our goal is to develop a visual tracking algorithm that is able to track moving objects in the presence of illumination variations in the scene and that is robust to occlusions. We treat the illumination and motion ( x-y translation and scale) parameters as the unknown "state" sequence. The observation is the entire image, and the observation model allows for occasional occlusions (modeled as outliers). The nonlinearity and multimodality of the observation model necessitate the use of a particle filter (PF). Due to the inclusion of illumination parameters, the state dimension increases, thus making regular PFs impractically expensive. We show that the recently proposed approach using a PF with a mode tracker can be used here since, even in most occlusion cases, the posterior of illumination conditioned on motion and the previous state is unimodal and quite narrow. The key idea is to importance sample on the motion states while approximating importance sampling by posterior mode tracking for estimating illumination. Experiments demonstrate the advantage of the proposed algorithm over existing PF-based approaches for various face and vehicle tracking. We are also able to detect illumination model changes, e.g., those due to transition from shadow to sunlight or vice versa by using the generalized expected log-likelihood statistics and successfully compensate for it without ever loosing track.

Resumo Limpo

correspond goal develop visual track algorithm abl track move object presenc illumin variat scene robust occlus treat illumin motion xy translat scale paramet unknown state sequenc observ entir imag observ model allow occasion occlus model outlier nonlinear multimod observ model necessit use particl filter pf due inclus illumin paramet state dimens increas thus make regular pfs impract expens show recent propos approach use pf mode tracker can use sinc even occlus case posterior illumin condit motion previous state unimod quit narrow key idea import sampl motion state approxim import sampl posterior mode track estim illumin experi demonstr advantag propos algorithm exist pfbase approach various face vehicl track also abl detect illumin model chang eg due transit shadow sunlight vice versa use general expect loglikelihood statist success compens without ever loos track

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