Static video compression's influence on neural network performance
Gowrisetty, Vishnu Sai Sankeerth and Fernando, Anil (2022) Static video compression's influence on neural network performance. Electronics, 12 (1). 8. ISSN 2079-9292 (https://doi.org/10.3390/electronics12010008)
Preview |
Text.
Filename: Gowrisetty_Fernando_Electronics_2022_Static_video_compressions_influence_on_neural.pdf
Final Published Version License: Download (4MB)| Preview |
Abstract
The concept of action recognition in smart security heavily relies on deep learning and artificial intelligence to make predictions about actions of humans. To draw appropriate conclusions from these hypotheses, a large amount of information is required. The data in question are often a video feed, and there is a direct relationship between increased data volume and more-precise decision-making. We seek to determine how far a static video can be compressed before the neural network's capacity to predict the action in the video is lost. To find this, videos are compressed by lowering the bitrate using FFMPEG. In parallel, a convolutional neural network model is trained to recognise action in the videos and is tested on the compressed videos until the neural network fails to predict the action observed in the videos. The results reveal that bitrate compression has no linear relationship with neural network performance.
-
-
Item type: Article ID code: 83733 Dates: DateEvent20 December 2022Published10 December 2022Accepted1 August 2022SubmittedSubjects: Science > Mathematics > Computer software Department: Faculty of Science > Computer and Information Sciences Depositing user: Pure Administrator Date deposited: 16 Jan 2023 16:09 Last modified: 04 Sep 2024 01:18 URI: https://strathprints.strath.ac.uk/id/eprint/83733