A neuro-inspired visual tracking method based on programmable system-on-chip platform
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 bo...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yang, Shufan [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2017 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© The Natural Computing Applications Forum 2017 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 30(2017), 9 vom: 20. Jan., Seite 2697-2708 |
|---|---|
| Übergeordnetes Werk: |
volume:30 ; year:2017 ; number:9 ; day:20 ; month:01 ; pages:2697-2708 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00521-017-2847-5 |
|---|
| Katalog-ID: |
OLC2025608454 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2025608454 | ||
| 003 | DE-627 | ||
| 005 | 20230502114759.0 | ||
| 007 | tu | ||
| 008 | 200820s2017 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s00521-017-2847-5 |2 doi | |
| 035 | |a (DE-627)OLC2025608454 | ||
| 035 | |a (DE-He213)s00521-017-2847-5-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 004 |q VZ |
| 100 | 1 | |a Yang, Shufan |e verfasserin |0 (orcid)0000-0003-0531-2903 |4 aut | |
| 245 | 1 | 0 | |a A neuro-inspired visual tracking method based on programmable system-on-chip platform |
| 264 | 1 | |c 2017 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © The Natural Computing Applications Forum 2017 | ||
| 520 | |a 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. | ||
| 650 | 4 | |a Visual object tracking | |
| 650 | 4 | |a Mean-shift | |
| 650 | 4 | |a Level set | |
| 650 | 4 | |a Attractor neural network model | |
| 650 | 4 | |a Occlusion | |
| 650 | 4 | |a System-on-chip | |
| 700 | 1 | |a Wong-Lin, KongFatt |4 aut | |
| 700 | 1 | |a Andrew, James |4 aut | |
| 700 | 1 | |a Mak, Terrence |4 aut | |
| 700 | 1 | |a McGinnity, T. Martin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Neural computing & applications |d Springer London, 1993 |g 30(2017), 9 vom: 20. Jan., Seite 2697-2708 |w (DE-627)165669608 |w (DE-600)1136944-9 |w (DE-576)032873050 |x 0941-0643 |7 nnns |
| 773 | 1 | 8 | |g volume:30 |g year:2017 |g number:9 |g day:20 |g month:01 |g pages:2697-2708 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s00521-017-2847-5 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_2018 | ||
| 912 | |a GBV_ILN_4046 | ||
| 912 | |a GBV_ILN_4277 | ||
| 951 | |a AR | ||
| 952 | |d 30 |j 2017 |e 9 |b 20 |c 01 |h 2697-2708 | ||
| author_variant |
s y sy k w l kwl j a ja t m tm t m m tm tmm |
|---|---|
| matchkey_str |
article:09410643:2017----::nuonprdiulrcigehdaeopormals |
| hierarchy_sort_str |
2017 |
| publishDate |
2017 |
| allfields |
10.1007/s00521-017-2847-5 doi (DE-627)OLC2025608454 (DE-He213)s00521-017-2847-5-p DE-627 ger DE-627 rakwb eng 004 VZ Yang, Shufan verfasserin (orcid)0000-0003-0531-2903 aut A neuro-inspired visual tracking method based on programmable system-on-chip platform 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Natural Computing Applications Forum 2017 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. Visual object tracking Mean-shift Level set Attractor neural network model Occlusion System-on-chip Wong-Lin, KongFatt aut Andrew, James aut Mak, Terrence aut McGinnity, T. Martin aut Enthalten in Neural computing & applications Springer London, 1993 30(2017), 9 vom: 20. Jan., Seite 2697-2708 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:30 year:2017 number:9 day:20 month:01 pages:2697-2708 https://doi.org/10.1007/s00521-017-2847-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 30 2017 9 20 01 2697-2708 |
| spelling |
10.1007/s00521-017-2847-5 doi (DE-627)OLC2025608454 (DE-He213)s00521-017-2847-5-p DE-627 ger DE-627 rakwb eng 004 VZ Yang, Shufan verfasserin (orcid)0000-0003-0531-2903 aut A neuro-inspired visual tracking method based on programmable system-on-chip platform 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Natural Computing Applications Forum 2017 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. Visual object tracking Mean-shift Level set Attractor neural network model Occlusion System-on-chip Wong-Lin, KongFatt aut Andrew, James aut Mak, Terrence aut McGinnity, T. Martin aut Enthalten in Neural computing & applications Springer London, 1993 30(2017), 9 vom: 20. Jan., Seite 2697-2708 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:30 year:2017 number:9 day:20 month:01 pages:2697-2708 https://doi.org/10.1007/s00521-017-2847-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 30 2017 9 20 01 2697-2708 |
| allfields_unstemmed |
10.1007/s00521-017-2847-5 doi (DE-627)OLC2025608454 (DE-He213)s00521-017-2847-5-p DE-627 ger DE-627 rakwb eng 004 VZ Yang, Shufan verfasserin (orcid)0000-0003-0531-2903 aut A neuro-inspired visual tracking method based on programmable system-on-chip platform 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Natural Computing Applications Forum 2017 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. Visual object tracking Mean-shift Level set Attractor neural network model Occlusion System-on-chip Wong-Lin, KongFatt aut Andrew, James aut Mak, Terrence aut McGinnity, T. Martin aut Enthalten in Neural computing & applications Springer London, 1993 30(2017), 9 vom: 20. Jan., Seite 2697-2708 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:30 year:2017 number:9 day:20 month:01 pages:2697-2708 https://doi.org/10.1007/s00521-017-2847-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 30 2017 9 20 01 2697-2708 |
| allfieldsGer |
10.1007/s00521-017-2847-5 doi (DE-627)OLC2025608454 (DE-He213)s00521-017-2847-5-p DE-627 ger DE-627 rakwb eng 004 VZ Yang, Shufan verfasserin (orcid)0000-0003-0531-2903 aut A neuro-inspired visual tracking method based on programmable system-on-chip platform 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Natural Computing Applications Forum 2017 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. Visual object tracking Mean-shift Level set Attractor neural network model Occlusion System-on-chip Wong-Lin, KongFatt aut Andrew, James aut Mak, Terrence aut McGinnity, T. Martin aut Enthalten in Neural computing & applications Springer London, 1993 30(2017), 9 vom: 20. Jan., Seite 2697-2708 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:30 year:2017 number:9 day:20 month:01 pages:2697-2708 https://doi.org/10.1007/s00521-017-2847-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 30 2017 9 20 01 2697-2708 |
| allfieldsSound |
10.1007/s00521-017-2847-5 doi (DE-627)OLC2025608454 (DE-He213)s00521-017-2847-5-p DE-627 ger DE-627 rakwb eng 004 VZ Yang, Shufan verfasserin (orcid)0000-0003-0531-2903 aut A neuro-inspired visual tracking method based on programmable system-on-chip platform 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Natural Computing Applications Forum 2017 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. Visual object tracking Mean-shift Level set Attractor neural network model Occlusion System-on-chip Wong-Lin, KongFatt aut Andrew, James aut Mak, Terrence aut McGinnity, T. Martin aut Enthalten in Neural computing & applications Springer London, 1993 30(2017), 9 vom: 20. Jan., Seite 2697-2708 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:30 year:2017 number:9 day:20 month:01 pages:2697-2708 https://doi.org/10.1007/s00521-017-2847-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 30 2017 9 20 01 2697-2708 |
| language |
English |
| source |
Enthalten in Neural computing & applications 30(2017), 9 vom: 20. Jan., Seite 2697-2708 volume:30 year:2017 number:9 day:20 month:01 pages:2697-2708 |
| sourceStr |
Enthalten in Neural computing & applications 30(2017), 9 vom: 20. Jan., Seite 2697-2708 volume:30 year:2017 number:9 day:20 month:01 pages:2697-2708 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Visual object tracking Mean-shift Level set Attractor neural network model Occlusion System-on-chip |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
Neural computing & applications |
| authorswithroles_txt_mv |
Yang, Shufan @@aut@@ Wong-Lin, KongFatt @@aut@@ Andrew, James @@aut@@ Mak, Terrence @@aut@@ McGinnity, T. Martin @@aut@@ |
| publishDateDaySort_date |
2017-01-20T00:00:00Z |
| hierarchy_top_id |
165669608 |
| dewey-sort |
14 |
| id |
OLC2025608454 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2025608454</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502114759.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2017 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00521-017-2847-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2025608454</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00521-017-2847-5-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yang, Shufan</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-0531-2903</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A neuro-inspired visual tracking method based on programmable system-on-chip platform</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Natural Computing Applications Forum 2017</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Visual object tracking</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mean-shift</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Level set</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Attractor neural network model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Occlusion</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">System-on-chip</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wong-Lin, KongFatt</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Andrew, James</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mak, Terrence</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">McGinnity, T. Martin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural computing & applications</subfield><subfield code="d">Springer London, 1993</subfield><subfield code="g">30(2017), 9 vom: 20. Jan., Seite 2697-2708</subfield><subfield code="w">(DE-627)165669608</subfield><subfield code="w">(DE-600)1136944-9</subfield><subfield code="w">(DE-576)032873050</subfield><subfield code="x">0941-0643</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:30</subfield><subfield code="g">year:2017</subfield><subfield code="g">number:9</subfield><subfield code="g">day:20</subfield><subfield code="g">month:01</subfield><subfield code="g">pages:2697-2708</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00521-017-2847-5</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">30</subfield><subfield code="j">2017</subfield><subfield code="e">9</subfield><subfield code="b">20</subfield><subfield code="c">01</subfield><subfield code="h">2697-2708</subfield></datafield></record></collection>
|
| author |
Yang, Shufan |
| spellingShingle |
Yang, Shufan ddc 004 misc Visual object tracking misc Mean-shift misc Level set misc Attractor neural network model misc Occlusion misc System-on-chip A neuro-inspired visual tracking method based on programmable system-on-chip platform |
| authorStr |
Yang, Shufan |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)165669608 |
| format |
Article |
| dewey-ones |
004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0941-0643 |
| topic_title |
004 VZ A neuro-inspired visual tracking method based on programmable system-on-chip platform Visual object tracking Mean-shift Level set Attractor neural network model Occlusion System-on-chip |
| topic |
ddc 004 misc Visual object tracking misc Mean-shift misc Level set misc Attractor neural network model misc Occlusion misc System-on-chip |
| topic_unstemmed |
ddc 004 misc Visual object tracking misc Mean-shift misc Level set misc Attractor neural network model misc Occlusion misc System-on-chip |
| topic_browse |
ddc 004 misc Visual object tracking misc Mean-shift misc Level set misc Attractor neural network model misc Occlusion misc System-on-chip |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Neural computing & applications |
| hierarchy_parent_id |
165669608 |
| dewey-tens |
000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Neural computing & applications |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 |
| title |
A neuro-inspired visual tracking method based on programmable system-on-chip platform |
| ctrlnum |
(DE-627)OLC2025608454 (DE-He213)s00521-017-2847-5-p |
| title_full |
A neuro-inspired visual tracking method based on programmable system-on-chip platform |
| author_sort |
Yang, Shufan |
| journal |
Neural computing & applications |
| journalStr |
Neural computing & applications |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2017 |
| contenttype_str_mv |
txt |
| container_start_page |
2697 |
| author_browse |
Yang, Shufan Wong-Lin, KongFatt Andrew, James Mak, Terrence McGinnity, T. Martin |
| container_volume |
30 |
| class |
004 VZ |
| format_se |
Aufsätze |
| author-letter |
Yang, Shufan |
| doi_str_mv |
10.1007/s00521-017-2847-5 |
| normlink |
(ORCID)0000-0003-0531-2903 |
| normlink_prefix_str_mv |
(orcid)0000-0003-0531-2903 |
| dewey-full |
004 |
| title_sort |
a neuro-inspired visual tracking method based on programmable system-on-chip platform |
| title_auth |
A neuro-inspired visual tracking method based on programmable system-on-chip platform |
| abstract |
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. © The Natural Computing Applications Forum 2017 |
| abstractGer |
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. © The Natural Computing Applications Forum 2017 |
| abstract_unstemmed |
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. © The Natural Computing Applications Forum 2017 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 |
| container_issue |
9 |
| title_short |
A neuro-inspired visual tracking method based on programmable system-on-chip platform |
| url |
https://doi.org/10.1007/s00521-017-2847-5 |
| remote_bool |
false |
| author2 |
Wong-Lin, KongFatt Andrew, James Mak, Terrence McGinnity, T. Martin |
| author2Str |
Wong-Lin, KongFatt Andrew, James Mak, Terrence McGinnity, T. Martin |
| ppnlink |
165669608 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00521-017-2847-5 |
| up_date |
2024-07-04T01:41:33.842Z |
| _version_ |
1803610796948717568 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2025608454</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502114759.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2017 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00521-017-2847-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2025608454</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00521-017-2847-5-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yang, Shufan</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-0531-2903</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A neuro-inspired visual tracking method based on programmable system-on-chip platform</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Natural Computing Applications Forum 2017</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Visual object tracking</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mean-shift</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Level set</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Attractor neural network model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Occlusion</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">System-on-chip</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wong-Lin, KongFatt</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Andrew, James</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mak, Terrence</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">McGinnity, T. Martin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural computing & applications</subfield><subfield code="d">Springer London, 1993</subfield><subfield code="g">30(2017), 9 vom: 20. Jan., Seite 2697-2708</subfield><subfield code="w">(DE-627)165669608</subfield><subfield code="w">(DE-600)1136944-9</subfield><subfield code="w">(DE-576)032873050</subfield><subfield code="x">0941-0643</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:30</subfield><subfield code="g">year:2017</subfield><subfield code="g">number:9</subfield><subfield code="g">day:20</subfield><subfield code="g">month:01</subfield><subfield code="g">pages:2697-2708</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00521-017-2847-5</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">30</subfield><subfield code="j">2017</subfield><subfield code="e">9</subfield><subfield code="b">20</subfield><subfield code="c">01</subfield><subfield code="h">2697-2708</subfield></datafield></record></collection>
|
| score |
7.4020987 |