AFD: A feature detection method for outdoor real-time video stitching system

This paper proposes an efficient feature detection solution called AFD (Auto-adapted Feature Detection) based on FAST and LDB applied to outdoor real-time video stitching system. As FAST features are sensitive to illumination and blur, AFD comes out to optimize it. AFD detects features with local and global auto-adapted FAST threshold. Global score compensation is adopted to get good performance under different outdoor situation as well. We use a multiple grid test method to generate more distinctive descriptors. In order to reduce the computing time and increase accuracy, features are extracted in the region of interest (ROI). The feature detection with low complexity and robustness performs well in the real-time stitching system.