Efficient image stitching for railway freight roof surveillance via regional feature matching

Wireless networks must be used for picture stitching by monitoring on both sides so as to expand the field of vision and provide a top panoramic image in order to ensure the safety of railroad freight. For the purpose to handle a great deal of samples and many features that arise in real-world scenarios, this study suggests an image stitching technique based on regional level feature matching and enhanced SIFT. The algorithm carries out multi-level matching through randomized K-D trees by increasing feature points, decreasing descriptor dimensionality, and partitioning feature matching regions. With the goal to enable panoramic stitching of freight vehicle photos, dynamic RANSAC thresholds construct the global homography matrix and remove incompatibility. Tests on actual railway datasets show that our approach speeds up processing, lowers mismatches, and boosts matching accuracy.