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Visual tracking with motion estimation and adaptive target appearance model embedded in particle filtering

Başer, Erkan
In this thesis we study Particle Filter for visual tracking applications. The sequential Monte Carlo methods or Particle Filter provides approximate solutions when the tracking problem involves non-linear and/or non-Gaussian state space models. Also in this study, in order to make the visual tracker robust against change in target appearance and unexpected target motion, an adaptive target appearance model and a first order motion estimator are embedded in particle filtering. Additionally, since pixels that don’t belong to target makes the motion estimation biased, the algorithm includes robust estimators to make the tracker reliable. Within the scope of this thesis the visual tracker proposed in [5] is implemented and the same problem is solved by proposing a Rao-Blackwellized Particle Filter. To deal with problems encountered during the implementation phase of the algorithm some improvements are made such as utilizing learning rate for the computation of adaptive velocity estimation. Moreover, some precautions are taken such as checking the velocity estimations to validate them. Finally, we have done several experiments both in indoor and outdoor environments to demonstrate the effectiveness and robustness of the implemented algorithm. Experimental results show that most of the time the visual tracker performs well. On the other hand the drawbacks of the implemented tracker are indicated and we explain how to eliminate them.