摘要
To improve the reliability and accuracy of visual tracker,a robust visual tracking algorithm based on multi-cues fusion under Bayesian framework is proposed.The weighed color and texture cues of the object are applied to describe the moving object.An adjustable observation model is incorporated into particle filtering,which utilizes the properties of particle filter for coping with non-linear,non-Gaussian assumption and the ability to predict the position of the moving object in a cluttered environment and two complementary attributes are employed to estimate the matching similarity dynamically in term of the likelihood ratio factors;furthermore tunes the weight values according to the confidence map of the color and texture feature on-line adaptively to reconfigure the optimal observation likelihood model,which ensured attaining the maximum likelihood ratio in the tracking scenario even if in the situations where the object is occluded or illumination,pose and scale are time-variant.The experimental result shows that the algorithm can track a moving object accurately while the reliability of tracking in a challenging case is validated in the experimentation.
To improve the reliability and accuracy of visual tracker, a robust visual tracking algorithm based on multi-cues fusion under Bayesian framework is proposed. The weighed color and texture cues of the object are applied to describe the moving object. An adjustable observation model is incorporated into particle filtering, which utilizes the properties of particle filter for coping with non-linear, non-Gaussian assumption and the ability to predict the position of the moving object in a cluttered environment and two complementary attributes are em- ployed to estimate the matching similarity dynamically in term of the likelihood ratio factors ; furthermore tunes the weight values according to the confidence map of the color and texture feature on-line adaptively to reconfigure the optimal observation likelihood model, which ensured attaining the maximum likelihood ratio in the tracking scenario even if in the situations where the object is occluded or illumination, pose and scale are time-variant. The experimental result shows that the algorithm can track a moving object accurately while the reliability of tracking in a challenging case is validated in the experimentation.
