An image captured by a single camera has a smaller viewing angle than that of the human eye. One method to expand this viewing angle is a technique known as image stitching, which generates a wider view from images captured with multiple cameras. Although this technique has found uses in multiple industries, it is vulnerable to parallax distortion, wherein objects disappear from or repeatedly appear in stitched images when the parallax between cameras differs significantly. To minimize parallax distortion, seam-based and multi-homography-based methods have been proposed. In particular, the seam-based method enables faster image stitching owing to its intuitive procedure; however, the seam generation matrix may still incur parallax distortion under certain restrictive circumstances, and a longer stitching time is required when this method is applied to video sequences. This motivated us to develop the Guided Energy–Depth Map, which uses the energy function, depth information, and guidance map to minimize parallax distortion from a human visual perspective and reduce the time required to apply the stitching process to video sequences. Based on Average Seam Error (ASE) evaluation, the proposed method produces better seams than energy functions in 25 out of 32 experimental datasets, and the improvement rate of ASE evaluation is 15.58%. Also, the Frame Selection module for video stitching proposed in this paper takes only 7.27% of the time to find a specific frame for seam regeneration compared to the instance segmentation-based frame selection method.