We propose an adaptive model update mechanism for face tracking based on mean-shift, we employ the Kalman filter to
predict a proper original position for mean shift tracking algorithm. To overcome the problem of appearance change, an
adaptive modal update is introduced. We classify the occlusion problems into two main cases specified as partial occlusion
and complete occlusion according to the number of similar sub blocks between object and candidate. We fuss Kalman
predictor into Mean-shift tracker in case of partial occlusion, for case of full occlusion, we divide object and candidate into
four parts respectively, according to the previous exact tracking result, we compute the average velocity of the target, and
then check the condition for face reappearing, with which we present an efficient target search strategy to deal with full
occlusion. Various tracking sequences demonstrate the superior behavior of our tracker and its robustness to appearance
changes and occlusions.
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