HSBS: A Human's Heat Signature and Background Subtraction Hybrid Approach for Crowd Counting and Analysis.
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| Title: | HSBS: A Human's Heat Signature and Background Subtraction Hybrid Approach for Crowd Counting and Analysis. |
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| Authors: | Negied, Nermin Kamal Abdel-Wahab1, Hemayed, Elsayed B.2, Fayek, Magda2 |
| Source: | International Journal of Pattern Recognition & Artificial Intelligence. Sep2016, Vol. 30 Issue 8, p-1. 24p. |
| Subjects: | Hybrid systems, Motion detectors, Prior learning, Camera calibration, Algorithms |
| Abstract: | This work presents a new approach for crowd counting and classification based upon human thermal and motion features. The technique is efficient for automatic crowd density estimation and type of motion determination. Crowd density is measured without any need for camera calibration or assumption of prior knowledge about the input videos. It does not need any human intervention so it can be used successfully in a fully automated crowd control systems. Two new features are introduced for crowd counting purpose: the first represents thermal characteristics of humans and is expressed by the ratio between their temperature and their ambient environment temperature. The second describes humans motion characteristics and is measured by the ratio between humans motion velocity and the ambient environment rigidity. Each ratio should exceed a certain predetermined threshold for human beings. These features have been investigated and proved to give accurate crowd counting performance in real time. Moreover, the two features are combined and used together for crowd classification into one of the three main types, which are: fully mobile, fully static, or mix of both types. Last but not least, the proposed system offers several advantages such as being a privacy preserving crowd counting system, reliable for homogeneous and inhomogeneous crowds, does not depend on a certain direction in motion detection, has no restriction on crowd size. The experimental results demonstrate the effectiveness of the approach. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 116856821 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: HSBS: A Human's Heat Signature and Background Subtraction Hybrid Approach for Crowd Counting and Analysis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Negied%2C+Nermin+Kamal+Abdel-Wahab%22">Negied, Nermin Kamal Abdel-Wahab</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Hemayed%2C+Elsayed+B%2E%22">Hemayed, Elsayed B.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Fayek%2C+Magda%22">Fayek, Magda</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Pattern+Recognition+%26+Artificial+Intelligence%22">International Journal of Pattern Recognition & Artificial Intelligence</searchLink>. Sep2016, Vol. 30 Issue 8, p-1. 24p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Hybrid+systems%22">Hybrid systems</searchLink><br /><searchLink fieldCode="DE" term="%22Motion+detectors%22">Motion detectors</searchLink><br /><searchLink fieldCode="DE" term="%22Prior+learning%22">Prior learning</searchLink><br /><searchLink fieldCode="DE" term="%22Camera+calibration%22">Camera calibration</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This work presents a new approach for crowd counting and classification based upon human thermal and motion features. The technique is efficient for automatic crowd density estimation and type of motion determination. Crowd density is measured without any need for camera calibration or assumption of prior knowledge about the input videos. It does not need any human intervention so it can be used successfully in a fully automated crowd control systems. Two new features are introduced for crowd counting purpose: the first represents thermal characteristics of humans and is expressed by the ratio between their temperature and their ambient environment temperature. The second describes humans motion characteristics and is measured by the ratio between humans motion velocity and the ambient environment rigidity. Each ratio should exceed a certain predetermined threshold for human beings. These features have been investigated and proved to give accurate crowd counting performance in real time. Moreover, the two features are combined and used together for crowd classification into one of the three main types, which are: fully mobile, fully static, or mix of both types. Last but not least, the proposed system offers several advantages such as being a privacy preserving crowd counting system, reliable for homogeneous and inhomogeneous crowds, does not depend on a certain direction in motion detection, has no restriction on crowd size. The experimental results demonstrate the effectiveness of the approach. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1142/S0218001416550259 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: -1 Subjects: – SubjectFull: Hybrid systems Type: general – SubjectFull: Motion detectors Type: general – SubjectFull: Prior learning Type: general – SubjectFull: Camera calibration Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: HSBS: A Human's Heat Signature and Background Subtraction Hybrid Approach for Crowd Counting and Analysis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Negied, Nermin Kamal Abdel-Wahab – PersonEntity: Name: NameFull: Hemayed, Elsayed B. – PersonEntity: Name: NameFull: Fayek, Magda IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 02180014 Numbering: – Type: volume Value: 30 – Type: issue Value: 8 Titles: – TitleFull: International Journal of Pattern Recognition & Artificial Intelligence Type: main |
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