Sequential Bayesian estimation techniques for the tracking problem in computer vision
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چکیده
— This paper presents review of techniques and algorithms used for filtering and data association in visual tracking. Kalman filter is an optimal Bayesian filter for linear dynamic models with Gaussian noise. Most of the processes and systems in real world are nonlinear, and in these situations there is extension of Kalman filter named the Extended Kalman filter (EKF). In case when the noise is non-Gaussian and/or nonlinearities are more severe, there is approach proved as more reliable for these tracking problems, known as Particle filtering. This paper aims to present the mathematical description of all presented algorithms and performance analysis and simulation results are given.
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