Real-time video fusion based on multistage hashing and hybrid transformation with depth adjustment.

Concatenating multicamera videos with differing centers of projection into a single panoramic video is a critical technology of many important applications. We propose a real-time video fusion approach to create wide field-of-view video. To provide a fast and accurate video registration method, we propose multistage hashing to find matched feature-point pairs from coarse to fine. In the first stage of multistage hashing, a short compact binary code is learned from all feature points, and then we calculate the Hamming distance between each two points to find the candidate-matched points. In the second stage, a long binary code is obtained by remapping the candidate points for fine matching. To tackle the distortion and scene depth variation of multiview frames in videos, we build hybrid transformation with depth adjustment. The depth compensation between two adjacent frames extends into multiple frames in an iterative model for successive video frames. We conduct several experiments with different dynamic scenes and camera numbers to verify the performance of the proposed real-time video fusion approach. [ABSTRACT FROM AUTHOR]