In order to improve the accuracy of local stereo matching, a stereo matching algorithm is proposed, which is based on improved Census transformation and adaptive support region. To solve the problem that the traditional Census transformation algorithm is sensitive to the sampling at a center point and has a high mismatching rate, this paper proposes an improved Census transformation algorithm which is insensitive to sampling by combining the information of the interpolation points at the left and right of a center point. In the stage of matching cost calculation, the improved Census transformation is combined with the color information and gradient information in x and y directions to construct the matching cost. In the stage of cost aggregation, a cross-based approach based on improved guided filtering is proposed to construct adaptive support regions and aggregate costs. Finally, the WTA strategy is used to calculate disparity, and the final disparity map is obtained through a multi-step refinement. The experimental results show that the algorithm proposed here has an average mismatch rate of 4.92% in four sets of standard images on the Middlebury test platform, indicating that it has high accuracy and good adaptability.