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Camera global motion estimation is critical to the success of video stabilization. This paper presents an effective and robust feature based motion estimation method. In the proposed approach, feature points are collected from input video sequences based on Speeded Up Robust Features (SURF). Random Samples Consensus (RANSAC) is used to remove local motion vectors and incorrect correspondences. In the global motion estimation, a particle filter is used to estimate the weight of feature points, solving the issue of Different Depth of Field (DDOF) for feature points. Then, the weighted least square (WLS) algorithm is applied to obtain the global motion estimation. Finally, a Kalman filter estimates the intentional motion, and the unintentional motion is compensated to obtain stable video sequences. Experimental results show that the proposed algorithm has the characteristics of high precision and good robustness.