An intelligent automatic human detection and tracking system based on weighted resampling particle filtering

Chang, LC; Pare, S; Meena, MS; Jain, D; Li, DL; Saxena, A; Prasad, M; Lin, CT

An intelligent automatic human detection and tracking system based on weighted resampling particle filtering

Chang, LC Pare, S Meena, MS Jain, D Li, DL Saxena, A Prasad, M

Lin, CT

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Big Data and Cognitive Computing, 2020, 4, (4), pp. 1-23
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Chang, LC
dc.contributor.author	Pare, S
dc.contributor.author	Meena, MS
dc.contributor.author	Jain, D
dc.contributor.author	Li, DL
dc.contributor.author	Saxena, A
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253
dc.contributor.author	Lin, CT
dc.date.accessioned	2020-12-20T19:44:36Z
dc.date.available	2020-12-20T19:44:36Z
dc.date.issued	2020-01-01
dc.identifier.citation	Big Data and Cognitive Computing, 2020, 4, (4), pp. 1-23
dc.identifier.issn	2504-2289
dc.identifier.issn	2504-2289
dc.identifier.uri	http://hdl.handle.net/10453/144876
dc.description.abstract	© 2020 by the authors. Licensee MDPI, Basel, Switzerland. At present, traditional visual‐based surveillance systems are becoming impractical, inefficient, and time‐consuming. Automation‐based surveillance systems appeared to overcome these limitations. However, the automatic systems have some challenges such as occlusion and retaining images smoothly and continuously. This research proposes a weighted resampling particle filter approach for human tracking to handle these challenges. The primary functions of the proposed system are human detection, human monitoring, and camera control. We used the codebook matching algorithm to define the human region as a target and track it, and we used the practical filter algorithm to follow and extract the target information. Consequently, the obtained information was used to configure the camera control. The experiments were tested in various environments to prove the stability and performance of the proposed system based on the active camera.
dc.language	en
dc.publisher	MDPI AG
dc.relation	http://purl.org/au-research/grants/arc/DP180100670
dc.relation	http://purl.org/au-research/grants/arc/DP180100656
dc.relation.ispartof	Big Data and Cognitive Computing
dc.relation.isbasedon	10.3390/bdcc4040027
dc.rights	info:eu-repo/semantics/openAccess
dc.title	An intelligent automatic human detection and tracking system based on weighted resampling particle filtering
dc.type	Journal Article
utslib.citation.volume	4
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2020-12-20T19:44:10Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	4
utslib.citation.issue	4

Abstract:

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. At present, traditional visual‐based surveillance systems are becoming impractical, inefficient, and time‐consuming. Automation‐based surveillance systems appeared to overcome these limitations. However, the automatic systems have some challenges such as occlusion and retaining images smoothly and continuously. This research proposes a weighted resampling particle filter approach for human tracking to handle these challenges. The primary functions of the proposed system are human detection, human monitoring, and camera control. We used the codebook matching algorithm to define the human region as a target and track it, and we used the practical filter algorithm to follow and extract the target information. Consequently, the obtained information was used to configure the camera control. The experiments were tested in various environments to prove the stability and performance of the proposed system based on the active camera.

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http://hdl.handle.net/10453/144876