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

Shufan Yang, KongFatt Wong-Lin, James Andrew, Terrence Mak, TM McGinnity

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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-initialization 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.
Original languageEnglish
Pages (from-to)2697-2708
JournalNeural Computing and Applications
Issue number9
Early online date20 Jan 2017
Publication statusPublished (in print/issue) - Nov 2018


  • Visual object tracking
  • mean-shift
  • level set
  • attractor neural network model
  • occlusion
  • system-on-chip


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