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A neuro-inspired visual tracking method based on programmable system-on-chip platform

A neuro-inspired visual tracking method based on programmable system-on-chip platform
A neuro-inspired visual tracking method based on programmable system-on-chip platform

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.

Attractor neural network model, Level set, Mean-shift, Occlusion, System-on-chip, Visual object tracking
0941-0643
2697-2708
Yang, Shufan
29598279-7d3f-415a-a42c-054c87b47d7d
Wong-Lin, Kong Fatt
807353f5-9562-4f39-bab4-1834221134cc
Andrew, James
e06c6dab-82c3-49b1-b856-b0cb2a6ad520
Mak, Terrence
0f90ac88-f035-4f92-a62a-7eb92406ea53
McGinnity, T. Martin
16e1716a-0b10-4f74-bfa6-55b497498447
Yang, Shufan
29598279-7d3f-415a-a42c-054c87b47d7d
Wong-Lin, Kong Fatt
807353f5-9562-4f39-bab4-1834221134cc
Andrew, James
e06c6dab-82c3-49b1-b856-b0cb2a6ad520
Mak, Terrence
0f90ac88-f035-4f92-a62a-7eb92406ea53
McGinnity, T. Martin
16e1716a-0b10-4f74-bfa6-55b497498447

Yang, Shufan, Wong-Lin, Kong Fatt, Andrew, James, Mak, Terrence and McGinnity, T. Martin (2018) A neuro-inspired visual tracking method based on programmable system-on-chip platform. Neural Computing and Applications, 30 (9), 2697-2708. (doi:10.1007/s00521-017-2847-5).

Record type: Article

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.

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More information

Accepted/In Press date: 5 January 2017
e-pub ahead of print date: 20 January 2017
Published date: 1 November 2018
Keywords: Attractor neural network model, Level set, Mean-shift, Occlusion, System-on-chip, Visual object tracking

Identifiers

Local EPrints ID: 426501
URI: http://eprints.soton.ac.uk/id/eprint/426501
ISSN: 0941-0643
PURE UUID: 647ad783-745f-4e3f-b94e-d7413af7bd70

Catalogue record

Date deposited: 29 Nov 2018 17:30
Last modified: 25 Nov 2021 20:50

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Contributors

Author: Shufan Yang
Author: Kong Fatt Wong-Lin
Author: James Andrew
Author: Terrence Mak
Author: T. Martin McGinnity

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