A Fog-Assisted Framework for Intelligent Video Preprocessing in Cloud-Based Video Surveillance as a Service
Siddharth Ravindran, Gnanasekaran Aghila · IEEE Transactions on Sustainable Computing · 2022
Video preprocessing is one of the vital steps before extracting useful information from surveillance videos. This article identifies the key issue in cloud-based video surveillance as a service (VSaaS). If the recording environment is almost static and storing 24/7 video recording leads to additional storage, computational and network complexity to the cloud user. To address this problem, the existing architecture of the VSaaS is thoroughly studied and proposed a fog-assisted framework for preprocessing the incoming video. The two important functionalities of this framework are intelligent frame-selection and frame-reconstruction. During frame-selection, the lightweight background subtraction algorithm based on random projection with structural similarity index manages to effectively select the necessary frames to store in the cloud. This framework also provides an option for the user to successfully reconstruct the full-length video using frame inbetweening method. The proposed algorithm is tested with CDNET-2014 and LASIESTA data. The results are evaluated in-terms of running time, selected frames per video, percentage of reduction and quality of video experiencing. The result shows that the proposed frame-selection manages to reduce nearly 60% of storage requirement for the CDNET-2014 data. The frame reconstruction algorithm reconstructs the full-length video without affecting the quality of video experiencing.