| Summary: | Video stitching technology provides an effective solution for a wide viewing angle monitoring mode for industrial applications. At present, the observation angle of a single camera is limited, and the monitoring network composed of multiple cameras will have many overlapping images captured. Monitoring surveillance cameras can cause the problems of viewing fatigue and low video utilization rate of involved personnel. In addition, current video stitching technology has poor adaptability and real-time performance. We propose an effective hybrid image feature detection method for fast video stitching of mine surveillance video using the effective information of the surveillance video captured from multiple cameras in the actual conditions in the industrial coal mine. The method integrates the Moravec corner point detection and the scale-invariant feature transform (SIFT) feature extractor. After feature extraction, the nearest neighbor method and the random sampling consistency (RANSAC) algorithm are used to register the video frames. The proposed method reduces the image stitching time and solves the problem of feature re-extraction due to the change of observation angle, thus optimizing the entire video stitching process. The experimental results on the real-world underground mine videos show that the optimized stitching method can stitch videos at a speed of 21 fps, effectively meeting the real-time requirement, while the stitching effect has a good stability and applicability in real-world conditions.
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