Static Video Compression’s Influence on Neural Network Performance

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,...

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Main Authors: Vishnu Sai Sankeerth Gowrisetty, Anil Fernando
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Electronics
Subjects:
Online Access:https://www.mdpi.com/2079-9292/12/1/8
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author Vishnu Sai Sankeerth Gowrisetty
Anil Fernando
author_facet Vishnu Sai Sankeerth Gowrisetty
Anil Fernando
author_sort Vishnu Sai Sankeerth Gowrisetty
collection DOAJ
description 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.
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spelling doaj.art-acaeba10d5884eabb6638814532ba2f52023-11-16T15:09:51ZengMDPI AGElectronics2079-92922022-12-01121810.3390/electronics12010008Static Video Compression’s Influence on Neural Network PerformanceVishnu Sai Sankeerth Gowrisetty0Anil Fernando1Department of Computer & Information Sciences, University of Strathclyde, Glasgow G1 1XQ, UKDepartment of Computer & Information Sciences, University of Strathclyde, Glasgow G1 1XQ, UKThe 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.https://www.mdpi.com/2079-9292/12/1/8bitrate compressionconvolutional neural networkaction recognition
spellingShingle Vishnu Sai Sankeerth Gowrisetty
Anil Fernando
Static Video Compression’s Influence on Neural Network Performance
Electronics
bitrate compression
convolutional neural network
action recognition
title Static Video Compression’s Influence on Neural Network Performance
title_full Static Video Compression’s Influence on Neural Network Performance
title_fullStr Static Video Compression’s Influence on Neural Network Performance
title_full_unstemmed Static Video Compression’s Influence on Neural Network Performance
title_short Static Video Compression’s Influence on Neural Network Performance
title_sort static video compression s influence on neural network performance
topic bitrate compression
convolutional neural network
action recognition
url https://www.mdpi.com/2079-9292/12/1/8
work_keys_str_mv AT vishnusaisankeerthgowrisetty staticvideocompressionsinfluenceonneuralnetworkperformance
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