Human detection using illumination invariant feature extraction for natural scenes in big data video frames

Alzughaibi, A; Chaczko, Z

Human detection using illumination invariant feature extraction for natural scenes in big data video frames

Alzughaibi, A Chaczko, Z

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 25th International Conference on Systems Engineering, ICSEng 2017, 2017, 2017-January pp. 443 - 450
Issue Date:: 2017-11-27

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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	2017-11-27	en_US
dc.identifier.citation	Proceedings - 25th International Conference on Systems Engineering, ICSEng 2017, 2017, 2017-January pp. 443 - 450	en_US
dc.identifier.isbn	9781538606100	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126429
dc.description.abstract	© 2017 IEEE. This research proposes a reliable machine learning based computational solution for human detection. The proposed model is specifically applicable for illumination-variant natural scenes in big data video frames. In order to solve the illumination variation problem, a new feature set is formed by extracting features using histogram of gradients (HoG) and linear phase quantization (LPQ) techniques, which are combined to form a single feature-set to describe features in illumination variant natural scenes. Pre-processing is applied to reduce the search space and improve results, and as the humans are in constant motion in the frames, a search space pruning algorithm is applied to reduce the search space and improve detection accuracy. Non-maximal suppression is also applied for improved performance. A Support Vector Machine (SVM) based classifier is used for fast and accurate detection. Most of the current state-of-the-art detectors face numerous problems including false, missed, and inaccurate detections. The proposed detector model shows good performance, which was validated using relevant UCF and CDW test data-sets. In order to compare the performance of the proposed methodology with the state-of-the-art detectors, some selected detected frames were chosen considering their Receiver Operating Characteristic (ROC) curves. These curves are plotted to compare and evaluate the results based on miss rates and true positives rates. The results show the proposed model achieves best results.	en_US
dc.relation.ispartof	Proceedings - 25th International Conference on Systems Engineering, ICSEng 2017	en_US
dc.relation.isbasedon	10.1109/ICSEng.2017.18	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Human detection using illumination invariant feature extraction for natural scenes in big data video frames	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2017-January	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-January	en_US

Abstract:

© 2017 IEEE. This research proposes a reliable machine learning based computational solution for human detection. The proposed model is specifically applicable for illumination-variant natural scenes in big data video frames. In order to solve the illumination variation problem, a new feature set is formed by extracting features using histogram of gradients (HoG) and linear phase quantization (LPQ) techniques, which are combined to form a single feature-set to describe features in illumination variant natural scenes. Pre-processing is applied to reduce the search space and improve results, and as the humans are in constant motion in the frames, a search space pruning algorithm is applied to reduce the search space and improve detection accuracy. Non-maximal suppression is also applied for improved performance. A Support Vector Machine (SVM) based classifier is used for fast and accurate detection. Most of the current state-of-the-art detectors face numerous problems including false, missed, and inaccurate detections. The proposed detector model shows good performance, which was validated using relevant UCF and CDW test data-sets. In order to compare the performance of the proposed methodology with the state-of-the-art detectors, some selected detected frames were chosen considering their Receiver Operating Characteristic (ROC) curves. These curves are plotted to compare and evaluate the results based on miss rates and true positives rates. The results show the proposed model achieves best results.

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http://hdl.handle.net/10453/126429