Depth estimation from captured video sequence needs a high time complexity. If we select a large size of window kernel for depth estimation, it will also affect to the computational time. Especially, in case of the depth estimation from sequential images, time complexity is a critical problem. In this paper, we propose a temporal domain stereo matching method for real-time depth estimation. Since the sequential image has a many similar region between neighboring frames, we use that properties for restricting a disparity search range. Even the relationship exists between the neighboring frames, following frame depth estimation result includes a small part of error. Eventually, the propagated error affect to accuracy of estimated depth value. Compensation method of error propagation is proposed based on the feature point in stereo image. Depth values are periodically estimated with maximum disparity search range. Since computing a cost value for all disparity search range needs a high time complexity, we restrict the disparity map renewal frequency. Experiment results show that the proposed depth estimation method in sequential image can derive more accurate depth value than conventional method.