Foreground Based Borderline Adjusting for Real Time Multi-camera Video Stitching

In this paper, we propose a multi-camera video stitching approach, which conducts borderline adjusting based on foreground information. In real time applications of video stitching, one problem is to deal with dynamic scenes, where foreground objects often cause broken objects like artifacts in panoramic video. To address this problem, we propose a foreground based borderline adjusting method to achieve smooth video stitching. In this method, foreground objects are extracted from different viewpoints, and the stitching borderlines are adjusted according to the foreground content in dynamic scenes. Guided by the adjusted borderlines, foreground objects are smoothly synthesized into panoramic video. Experiment results show that this approach obtains more than 81% correct rate for dynamic scenes, compared with 25%~50% correct rate on the condition of not utilizing foreground information, and achieves processing speed of 10~13 frames per second.