A Smart multi-view panoramic imaging integrating stitching with geometric matrix relations among surveillance cameras (SMPI)

Abstract

Reducing data stored and transferred is a critical topic in the modern era, particularly after the evolution in multimedia applications and surveillance systems worldwide. Motivated by the massive amount of data generated by surveillance cameras and the enormous number of redundant pixels produced among them, this paper introduces a novel model entitled: “A Smart Multi-View Panoramic Imaging integrating stitching with geometric matrix relations among surveillance cameras (SMPI).” The introduced model aims to create a novel feedback real-time stitching system to reduce the storing and transferring of redundant data generated by neighboring surveillance cameras for an extra level of compression. Moreover, the panoramic view is mostly a better monitoring option rather than multiple monitors in complicated surveillance cameras’ control rooms. The proposed system, in this paper, merges feature extraction stitching techniques with geometric relational matrix calculations to reduce the time complexity limitations of traditional mosaicking. Additionally, the proposed work introduces a real-time algorithm to reconstruct images of each camera from the panoramic view, and a novel algorithm for ordering cameras’ frames before stitching is recommended for producing a panoramic view without any human interference. The experimental work tests numerous state of the art feature extraction algorithms for stitching, Scale Invariant Feature Transform (SIFT), Speed Up Robust Feature (SURF) and Oriented FAST and Rotated BRIEF (ORB) with different orders of stitching. The amount of compression per image after reconstruction is also analyzed. The suggested model was implemented and tested using a vast number of benchmark datasets. Evaluation measures have been used to indicate the efficiency of the recommended system. The proposed model’s algorithm has recorded a low time processing per frame while keeping high accurate results. It was found that the recommended Efficient Stitching Algorithm (ESA) produced an average of 46 panoramas per second, and the reconstruction phase could reach a rate of 90 frames per second, which is significantly higher than the 30 frames per second standard video format system. These results give our model an excellent advantage for the effective processing of more scalable systems with a higher number of frames per second. The proposed system created panoramas with an average of 99% similarities with the traditional mosaicking systems while being highly faster than these conventional methods. Compression ratios and data rate savings, reflecting the gain in data stored and transferred, were calculated, reporting an average of 2.66 and 0.62 per frame, respectively, when applied to standard datasets. The results illustrated that the proposed system gives a dramatic reduction in the volume of data stored/transferred and showed that the creating of mosaics and the reconstruction was made in proper processing time. Experimental outcomes also showed that, for the suggested methods, the produced frames after reconstruction have a high similarity percentage compared with original ones before stitching, which indicates that the proposed approach is efficient enough to preserve the essential features of cameras’ frames without significant information loss.