Behavioral Analysis and Individual Tracking Based on Kalman Filter: Application in an Urban Environment

In order to improve behavioral analysis systems in urban environments, this paper proposes, using data extracted from video surveillance cameras, a tracking method through two approaches. The first approach consists in comparing the position of people between two images of a video and to perform tra...

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Autores principales:	Amaury Auguste, Wissam Kaddah, Marwa Elbouz, Ghislain Oudinet, Ayman Alfalou
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Kalman filters video tracking behavioral analysis YOLO anonymity data analysis Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/544c6c29dfb9410fae1afbf269cb95b5
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id	oai:doaj.org-article:544c6c29dfb9410fae1afbf269cb95b5
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spelling	oai:doaj.org-article:544c6c29dfb9410fae1afbf269cb95b52021-11-11T19:12:18ZBehavioral Analysis and Individual Tracking Based on Kalman Filter: Application in an Urban Environment10.3390/s212172341424-8220https://doaj.org/article/544c6c29dfb9410fae1afbf269cb95b52021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7234https://doaj.org/toc/1424-8220In order to improve behavioral analysis systems in urban environments, this paper proposes, using data extracted from video surveillance cameras, a tracking method through two approaches. The first approach consists in comparing the position of people between two images of a video and to perform tracking by proximity. The second method using Kalman filters is based on the anticipation of the position of an individual in the upcoming image. The use of this method proves to be more efficient as it allows continuing a detection even when people cross each other or when they pass behind obstacles. The use of Kalman filters in this domain provides a new approach to obtain reliable tracking and information on speed and trajectory variations. The proposed method is innovative in the way the tracking is performed and the results are exploited. Experiments were conducted in a real situation and showed that the use of some elements of the first method could be reused to integrate a notion of distance in the method based on the Kalman filter and thus improve the latter both in tracking and in detecting of abnormal behavior. This article deals with the functioning of the two methods as well as the results obtained with the same scenarios. The experimentation concludes through concrete results that the Kalman filter method is more efficient than the proximity method alone. A sample result is available online for two of the seven videos used in this article (accessed on 19 July 2021).Amaury AugusteWissam KaddahMarwa ElbouzGhislain OudinetAyman AlfalouMDPI AGarticleKalman filtersvideo trackingbehavioral analysisYOLOanonymitydata analysisChemical technologyTP1-1185ENSensors, Vol 21, Iss 7234, p 7234 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Kalman filters video tracking behavioral analysis YOLO anonymity data analysis Chemical technology TP1-1185
spellingShingle	Kalman filters video tracking behavioral analysis YOLO anonymity data analysis Chemical technology TP1-1185 Amaury Auguste Wissam Kaddah Marwa Elbouz Ghislain Oudinet Ayman Alfalou Behavioral Analysis and Individual Tracking Based on Kalman Filter: Application in an Urban Environment
description	In order to improve behavioral analysis systems in urban environments, this paper proposes, using data extracted from video surveillance cameras, a tracking method through two approaches. The first approach consists in comparing the position of people between two images of a video and to perform tracking by proximity. The second method using Kalman filters is based on the anticipation of the position of an individual in the upcoming image. The use of this method proves to be more efficient as it allows continuing a detection even when people cross each other or when they pass behind obstacles. The use of Kalman filters in this domain provides a new approach to obtain reliable tracking and information on speed and trajectory variations. The proposed method is innovative in the way the tracking is performed and the results are exploited. Experiments were conducted in a real situation and showed that the use of some elements of the first method could be reused to integrate a notion of distance in the method based on the Kalman filter and thus improve the latter both in tracking and in detecting of abnormal behavior. This article deals with the functioning of the two methods as well as the results obtained with the same scenarios. The experimentation concludes through concrete results that the Kalman filter method is more efficient than the proximity method alone. A sample result is available online for two of the seven videos used in this article (accessed on 19 July 2021).
format	article
author	Amaury Auguste Wissam Kaddah Marwa Elbouz Ghislain Oudinet Ayman Alfalou
author_facet	Amaury Auguste Wissam Kaddah Marwa Elbouz Ghislain Oudinet Ayman Alfalou
author_sort	Amaury Auguste
title	Behavioral Analysis and Individual Tracking Based on Kalman Filter: Application in an Urban Environment
title_short	Behavioral Analysis and Individual Tracking Based on Kalman Filter: Application in an Urban Environment
title_full	Behavioral Analysis and Individual Tracking Based on Kalman Filter: Application in an Urban Environment
title_fullStr	Behavioral Analysis and Individual Tracking Based on Kalman Filter: Application in an Urban Environment
title_full_unstemmed	Behavioral Analysis and Individual Tracking Based on Kalman Filter: Application in an Urban Environment
title_sort	behavioral analysis and individual tracking based on kalman filter: application in an urban environment
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/544c6c29dfb9410fae1afbf269cb95b5
work_keys_str_mv	AT amauryauguste behavioralanalysisandindividualtrackingbasedonkalmanfilterapplicationinanurbanenvironment AT wissamkaddah behavioralanalysisandindividualtrackingbasedonkalmanfilterapplicationinanurbanenvironment AT marwaelbouz behavioralanalysisandindividualtrackingbasedonkalmanfilterapplicationinanurbanenvironment AT ghislainoudinet behavioralanalysisandindividualtrackingbasedonkalmanfilterapplicationinanurbanenvironment AT aymanalfalou behavioralanalysisandindividualtrackingbasedonkalmanfilterapplicationinanurbanenvironment
_version_	1718431583932776448

Behavioral Analysis and Individual Tracking Based on Kalman Filter: Application in an Urban Environment

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