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Neural Computing and Applications

, Volume 30, Issue 9, pp 2697–2708 | Cite as

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

  • Shufan YangEmail author
  • KongFatt Wong-Lin
  • James Andrew
  • Terrence Mak
  • T. Martin McGinnity
Original 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.

Keywords

Visual object tracking Mean-shift Level set Attractor neural network model Occlusion System-on-chip 

Notes

Acknowledgements

SY was supported by the Early Research Scheme Reward from University of Wolverhampton and National High Technology Research and Development Program from China. KFW-L and SY were supported by ASUR (1014-C4-Ph1-071).

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.School of Mathematics and Computer Science, Faculty of Science and Engineering, City CampusUniversity of WolverhamptonWolverhamptonEngland, UK
  2. 2.Intelligent Systems Research CentreUniversity of UlsterLondonderryUK
  3. 3.Department of Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUK
  4. 4.School of Science and TechnologyNottingham Trent UniversityNottinghamUK

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