A precise human detection model using combination of feature extraction techniques in a dynamic environment

Alzughaibi, A; Chaczko, Z

A precise human detection model using combination of feature extraction techniques in a dynamic environment

Alzughaibi, A Chaczko, Z

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Publication Type:: Conference Proceeding
Citation:: International Conference Image and Vision Computing New Zealand, 2018, 2017-December pp. 1 - 6
Issue Date:: 2018-07-03

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Field	Value	Language
dc.contributor.author	Alzughaibi, A	en_US
dc.contributor.author	Chaczko, Z https://orcid.org/0000-0002-2816-7510	en_US
dc.date.issued	2018-07-03	en_US
dc.identifier.citation	International Conference Image and Vision Computing New Zealand, 2018, 2017-December pp. 1 - 6	en_US
dc.identifier.isbn	9781538642764	en_US
dc.identifier.issn	2151-2191	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132969
dc.description.abstract	© 2017 IEEE. This research paper presents a Machine Learning-based human detection model focusing on improving precision of human movement conditions in video frames. The problem is addressed by focusing on pre-processing and an efficient feature extraction methodology. Combination of features are extracted, including histograms of gradients (HoG), histograms of colors (HoC), and histograms of bars (HoB). These featuresets are combined to form the finall feature vector that describes the human shape, and the Support Vector Machine (SVM)-based classifier is used for classification purposes. Improving the precision will allow the human movement detector to make better detections by reducing false positives and missed detections, which are the problems faced by current detection techniques. Training of the algorithm is done using the INRIA dataset and tested on sequences depicting conditions of moving humans in different environments. In the testing phase, the search space is reduced using an upper body detector, which is done using haar features. The reduced space is used to carry out human detection using the proposed feature extraction technique. The proposed detector approach performs well, and the number of missed detections are reduced. However, some false detections are still performed, but this is due the fact that some objects resemble humans. The proposed model is benchmarked with the current state-of-the-art detectors using a challenging test dataset, which is used to test the performance. The Receiver Operating Characteristic (ROC) curves for the precision-recall and true-positive rates are plotted to compare and evaluate the results. The proposed model outperforms most of the current state-of-the-art detectors.	en_US
dc.relation.ispartof	International Conference Image and Vision Computing New Zealand	en_US
dc.relation.isbasedon	10.1109/IVCNZ.2017.8402477	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A precise human detection model using combination of feature extraction techniques in a dynamic environment	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2017-December	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	2017-December	en_US

Abstract:

© 2017 IEEE. This research paper presents a Machine Learning-based human detection model focusing on improving precision of human movement conditions in video frames. The problem is addressed by focusing on pre-processing and an efficient feature extraction methodology. Combination of features are extracted, including histograms of gradients (HoG), histograms of colors (HoC), and histograms of bars (HoB). These featuresets are combined to form the finall feature vector that describes the human shape, and the Support Vector Machine (SVM)-based classifier is used for classification purposes. Improving the precision will allow the human movement detector to make better detections by reducing false positives and missed detections, which are the problems faced by current detection techniques. Training of the algorithm is done using the INRIA dataset and tested on sequences depicting conditions of moving humans in different environments. In the testing phase, the search space is reduced using an upper body detector, which is done using haar features. The reduced space is used to carry out human detection using the proposed feature extraction technique. The proposed detector approach performs well, and the number of missed detections are reduced. However, some false detections are still performed, but this is due the fact that some objects resemble humans. The proposed model is benchmarked with the current state-of-the-art detectors using a challenging test dataset, which is used to test the performance. The Receiver Operating Characteristic (ROC) curves for the precision-recall and true-positive rates are plotted to compare and evaluate the results. The proposed model outperforms most of the current state-of-the-art detectors.

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http://hdl.handle.net/10453/132969