Open Access
ARTICLE
A Personalized Video Synopsis Framework for Spherical Surveillance Video
S. Priyadharshini*, Ansuman Mahapatra
Department of Computer Science and Engineering, National Institute of Technology Puducherry, India
* Corresponding Author: S. Priyadharshini. Email:
Computer Systems Science and Engineering 2023, 45(3), 2603-2616. https://doi.org/10.32604/csse.2023.032506
Received 20 May 2022; Accepted 13 July 2022; Issue published 21 December 2022
Abstract
Video synopsis is an effective way to easily summarize long-recorded
surveillance videos. The omnidirectional view allows the observer to select the
desired fields of view (FoV) from the different FoV available for spherical surveillance video. By choosing to watch one portion, the observer misses out on the
events occurring somewhere else in the spherical scene. This causes the observer
to experience fear of missing out (FOMO). Hence, a novel personalized video
synopsis approach for the generation of non-spherical videos has been introduced
to address this issue. It also includes an action recognition module that makes it
easy to display necessary actions by prioritizing them. This work minimizes and
maximizes multiple goals such as loss of activity, collision, temporal consistency,
length, show, and important action cost respectively. The performance of the proposed framework is evaluated through extensive simulation and compared with
the state-of-art video synopsis optimization algorithms. Experimental results suggest that some constraints are better optimized by using the latest metaheuristic
optimization algorithms to generate compact personalized synopsis videos from
spherical surveillance videos.
Keywords
Cite This Article
S. Priyadharshini and A. Mahapatra, "A personalized video synopsis framework for spherical surveillance video,"
Computer Systems Science and Engineering, vol. 45, no.3, pp. 2603–2616, 2023.