Different from the traditional point target tracking, the extended-target tracking not only estimates the target’s kinematical state, but also estimates the target’s extension state, including target’s shape, size and orientation. Aimed at the problem of the inaccurate and nonlinear estimation of the target shape, an algorithm based on random hypersurface model (RHM) and Bernoulli filter is proposed. Firstly, the measurement source of target is modeled as an ellipse random hypersurface model, and then it is embedded into the Bernoulli filter to track an extend target in real time. Finally, Gamma distribution is applied into the filter to estimate the measurement rate for improving the accuracy of estimation. A cluster step is inserted into the measurement update using the distance partitioning method in order to reduce the computational complexity. Experiment result shows that the proposed algorithm outperforms the traditional Bernoulli filter in estimating the trajectory and shape of the target, and can be used for practical video tracking scenarios.