A neuro-inspired visual tracking method based on programmable system-on-chip platform.
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| Title: | A neuro-inspired visual tracking method based on programmable system-on-chip platform. |
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| Authors: | Yang, Shufan1 s.yang@wlv.ac.uk, Wong-Lin, KongFatt2, Andrew, James1, Mak, Terrence3, McGinnity, T. Martin4 |
| Source: | Neural Computing & Applications. Nov2018, Vol. 30 Issue 9, p2697-2708. 12p. |
| Subjects: | Systems on a chip testing, Image processing, Object tracking (Computer vision), Artificial neural networks, Iterative methods (Mathematics) |
| Abstract: | Using programmable system-on-chip to implement computer vision functions poses many challenges due to highly constrained resources in cost, size and power consumption. In this work, we propose a new neuro-inspired image processing model and implemented it on a system-on-chip Xilinx Z702c board. With the attractor neural network model to store the object’s contour information, we eliminate the computationally expensive steps in the curve evolution re-initialisation at every new iteration or frame. Our experimental results demonstrate that this integrated approach achieves accurate and robust object tracking, when they are partially or completely occluded in the scenes. Importantly, the system is able to process 640 by 480 videos in real-time stream with 30 frames per second using only one low-power Xilinx Zynq-7000 system-on-chip platform. This proof-of-concept work has demonstrated the advantage of incorporating neuro-inspired features in solving image processing problems during occlusion. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computing & Applications is the property of Springer Nature 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 132696759 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A neuro-inspired visual tracking method based on programmable system-on-chip platform. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yang%2C+Shufan%22">Yang, Shufan</searchLink><relatesTo>1</relatesTo><i> s.yang@wlv.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Wong-Lin%2C+KongFatt%22">Wong-Lin, KongFatt</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Andrew%2C+James%22">Andrew, James</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Mak%2C+Terrence%22">Mak, Terrence</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22McGinnity%2C+T%2E+Martin%22">McGinnity, T. Martin</searchLink><relatesTo>4</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Nov2018, Vol. 30 Issue 9, p2697-2708. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Systems+on+a+chip+testing%22">Systems on a chip testing</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Object+tracking+%28Computer+vision%29%22">Object tracking (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Iterative+methods+%28Mathematics%29%22">Iterative methods (Mathematics)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Using programmable system-on-chip to implement computer vision functions poses many challenges due to highly constrained resources in cost, size and power consumption. In this work, we propose a new neuro-inspired image processing model and implemented it on a system-on-chip Xilinx Z702c board. With the attractor neural network model to store the object’s contour information, we eliminate the computationally expensive steps in the curve evolution re-initialisation at every new iteration or frame. Our experimental results demonstrate that this integrated approach achieves accurate and robust object tracking, when they are partially or completely occluded in the scenes. Importantly, the system is able to process 640 by 480 videos in real-time stream with 30 frames per second using only one low-power Xilinx Zynq-7000 system-on-chip platform. This proof-of-concept work has demonstrated the advantage of incorporating neuro-inspired features in solving image processing problems during occlusion. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=132696759 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00521-017-2847-5 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 2697 Subjects: – SubjectFull: Systems on a chip testing Type: general – SubjectFull: Image processing Type: general – SubjectFull: Object tracking (Computer vision) Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Iterative methods (Mathematics) Type: general Titles: – TitleFull: A neuro-inspired visual tracking method based on programmable system-on-chip platform. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yang, Shufan – PersonEntity: Name: NameFull: Wong-Lin, KongFatt – PersonEntity: Name: NameFull: Andrew, James – PersonEntity: Name: NameFull: Mak, Terrence – PersonEntity: Name: NameFull: McGinnity, T. Martin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 30 – Type: issue Value: 9 Titles: – TitleFull: Neural Computing & Applications Type: main |
| ResultId | 1 |
