2.50
Hdl Handle:
http://hdl.handle.net/2436/620401
Title:
A neuro-inspired visual tracking method based on programmable system-on-chip platform
Authors:
Yang, Shufan ( 0000-0003-0531-2903 ) ; Wong-Lin, KongFatt; Andrew, James; Mak, Terrence; McGinnity, T. Martin
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.
Citation:
A neuro-inspired visual tracking method based on programmable system-on-chip platform 2017 Neural Computing and Applications
Publisher:
Springer
Journal:
Neural Computing and Applications
Issue Date:
20-Jan-2017
URI:
http://hdl.handle.net/2436/620401
DOI:
10.1007/s00521-017-2847-5
Additional Links:
http://link.springer.com/10.1007/s00521-017-2847-5
Type:
Article
Language:
en
ISSN:
0941-0643
Appears in Collections:
Computational Linguistics Group

Full metadata record

DC FieldValue Language
dc.contributor.authorYang, Shufanen
dc.contributor.authorWong-Lin, KongFatten
dc.contributor.authorAndrew, Jamesen
dc.contributor.authorMak, Terrenceen
dc.contributor.authorMcGinnity, T. Martinen
dc.date.accessioned2017-03-07T11:50:51Z-
dc.date.available2017-03-07T11:50:51Z-
dc.date.issued2017-01-20-
dc.identifier.citationA neuro-inspired visual tracking method based on programmable system-on-chip platform 2017 Neural Computing and Applicationsen
dc.identifier.issn0941-0643en
dc.identifier.doi10.1007/s00521-017-2847-5-
dc.identifier.urihttp://hdl.handle.net/2436/620401-
dc.description.abstractUsing 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.en
dc.language.isoenen
dc.publisherSpringeren
dc.relation.urlhttp://link.springer.com/10.1007/s00521-017-2847-5en
dc.rightsArchived with thanks to Neural Computing and Applicationsen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectVisual object trackingen
dc.subjectMean-shiften
dc.subjectLevel seten
dc.subjectAttractor neural network modelen
dc.subjectOcclusionen
dc.subjectSystem-on-chipen
dc.titleA neuro-inspired visual tracking method based on programmable system-on-chip platformen
dc.typeArticleen
dc.identifier.journalNeural Computing and Applicationsen
dc.date.accepted2017-01-10-
rioxxterms.funderInternalen
rioxxterms.identifier.projectUOW070317SYen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
rioxxterms.licenseref.startdate2018-01-20en
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