Panoramic Video Synopsis on Constrained Devices for Security Surveillance

As the global demand for surveillance cameras increases, the digital footage data also explicitly increases. Analyzing and extracting meaningful content from footage is a resource-depleting and laborious effort. The traditional video synopsis technique is used for constructing a small video by relocating the object in the time and space domains. However, it is computationally expensive, and the obtained synopsis suffers from jitter artifacts; thus, it cannot be hosted on a resource-constrained device. In this research, we propose a panoramic video synopsis framework to address and solve the problems of the efficient analysis of objects for better governance and storage. The surveillance system has multiple cameras sharing a common homography, which the proposed method leverages. The proposed method constructs a panorama by solving the broad viewpoints with significant deviations, collisions, and overlapping among the images. We embed a synopsis framework on the end device to reduce storage, networking, and computational costs. A neural network-based model stitches multiple camera feeds to obtain a panoramic structure from which only tubes with abnormal behavior were extracted and relocated in the space and time domains to construct a shorter video. Comparatively, the proposed model achieved a superior accuracy matching rate of 98.7% when stitching the images. The feature enhancement model also achieves better peak signal-to-noise ratio values, facilitating smooth synopsis construction.