In this paper, we present a visual pose tracking algorithm based on Monte Carlo sampling of special Euclidean group SE(3) and knowledge of a 3D model. In general, the relative pose of an object in 3D space can be described by sequential transformation matrices at each time step. Thus, the objective of this work is to find a transformation matrix in SE(3) so that the projection of an object transformed by this matrix coincides with an object of interest in the 2D image plane. To do this, first, the set of these transformation matrices is randomly generated via an autoregressive model. Next, 3D trans- formation is performed on a 3D model by these matrices. Finally, a region-based energy model is designed to evaluate the optimality of a transformed model’s projection. Experimental results demonstrate the robustness of the proposed method in several tracking scenarios.