On No-Reference Error Detection of an Image Stitching System Based on Error-Tolerance

Abstract

Image stitching technology can stitch images (video frames) from multiple cameras into a single panoramic image, allowing a single image to cover a larger field of view. Image(video) stitching technology has also been widely used in real-time video applications such as online conference, VR or even Advanced Driver Assistance Systems (ADAS). Reliability of this technology is therefore of great importance. In this work, for the first time, we address the issue of error detection of an image stitching system. We show that there are inherent error-tolerability in this system, and the detection focus should be the unacceptable errors that would result in poor stitching results. In this work we also investigate the acceptability evaluation of the stitching results. In particular, a no-reference error detection technique is proposed so that the detection of unacceptable errors can be achieved in an online manner without golden data for comparison. Our experimental results show that the detection (acceptability classification) accuracy of the proposed error detection technique achieves 98.2%. In addition, the incurred performance overhead of our technique is ignorable. There is only 0.4% increase on the stitching execution time.