Background segmentation in difficult weather conditions

Background segmentation is a process in which an algorithm removes the static background from an image. This allows only a changing section of the image. This process is important for motion detection or object tracking. In this article, an approach is proposed to compare several existing algorithms...

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Main Authors: Lukasz Karbowiak, Janusz Bobulski
Format: Article
Language:English
Published: PeerJ Inc. 2022-05-01
Series:PeerJ Computer Science
Subjects:
Online Access:https://peerj.com/articles/cs-962.pdf
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author Lukasz Karbowiak
Janusz Bobulski
author_facet Lukasz Karbowiak
Janusz Bobulski
author_sort Lukasz Karbowiak
collection DOAJ
description Background segmentation is a process in which an algorithm removes the static background from an image. This allows only a changing section of the image. This process is important for motion detection or object tracking. In this article, an approach is proposed to compare several existing algorithms for background segmentation under severe weather conditions. Three weather conditions were tested: falling snow, rain and a sunny windy day. The test algorithms were executed on a test video containing frames collected by a dedicated Raspberry Pi camera. The frames used in the tests included cars, bicycles, motorcycles, people, and trees. Preliminary results from these tests show interesting differences in detail detection and detection noise.
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spelling doaj.art-68070a74c8e4450bb0d0fd4600e411342022-12-22T00:40:32ZengPeerJ Inc.PeerJ Computer Science2376-59922022-05-018e96210.7717/peerj-cs.962Background segmentation in difficult weather conditionsLukasz KarbowiakJanusz BobulskiBackground segmentation is a process in which an algorithm removes the static background from an image. This allows only a changing section of the image. This process is important for motion detection or object tracking. In this article, an approach is proposed to compare several existing algorithms for background segmentation under severe weather conditions. Three weather conditions were tested: falling snow, rain and a sunny windy day. The test algorithms were executed on a test video containing frames collected by a dedicated Raspberry Pi camera. The frames used in the tests included cars, bicycles, motorcycles, people, and trees. Preliminary results from these tests show interesting differences in detail detection and detection noise.https://peerj.com/articles/cs-962.pdfImage processingComputer visionSegmentation
spellingShingle Lukasz Karbowiak
Janusz Bobulski
Background segmentation in difficult weather conditions
PeerJ Computer Science
Image processing
Computer vision
Segmentation
title Background segmentation in difficult weather conditions
title_full Background segmentation in difficult weather conditions
title_fullStr Background segmentation in difficult weather conditions
title_full_unstemmed Background segmentation in difficult weather conditions
title_short Background segmentation in difficult weather conditions
title_sort background segmentation in difficult weather conditions
topic Image processing
Computer vision
Segmentation
url https://peerj.com/articles/cs-962.pdf
work_keys_str_mv AT lukaszkarbowiak backgroundsegmentationindifficultweatherconditions
AT januszbobulski backgroundsegmentationindifficultweatherconditions