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A Spiking Neural Network Architecture for Object Tracking

  • Yihao Luo
  • Quanzheng Yi
  • Tianjiang WangEmail author
  • Ling Lin
  • Yan Xu
  • Jing Zhou
  • Caihong Yuan
  • Jingjuan Guo
  • Ping Feng
  • Qi Feng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11901)

Abstract

Spiking neural network (SNN) has the advantages of high computational efficiency, low energy consumption, low memory resource consumption, and easy hardware implementation. But its training algorithm is immature and inefficiency which limits the applications of SNN. In this paper, we propose a SNN architecture named SiamSNN for object tracking to avoid the training problems. Specifically, we propose a more comprehensive parameter conversion scheme with the processes of standardization, retraining, parameter transfer, and weight normalization, in order to convert a trained CNN to a similar SNN. Then we propose an encoder named Attention with Average Rate Over Time (AAR) in order to encoding images to spiking sequences. By using IF model, the accuracy decreases by only 0.007 on MNIST compared to the original method. Our approach applies SNN to object tracking and achieves certain effects, which is a reference for SNN applications in other computer vision areas in the future.

Keywords

Spiking neural network Object tracking Conversion Encoder 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yihao Luo
    • 1
  • Quanzheng Yi
    • 1
  • Tianjiang Wang
    • 1
    Email author
  • Ling Lin
    • 1
  • Yan Xu
    • 1
  • Jing Zhou
    • 1
    • 2
  • Caihong Yuan
    • 1
    • 3
  • Jingjuan Guo
    • 1
    • 4
  • Ping Feng
    • 1
    • 5
  • Qi Feng
    • 1
  1. 1.School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina
  2. 2.School of Mathematics and Computer ScienceJianghan UniversityWuhanChina
  3. 3.School of Computer and Information EngineeringHenan UniversityKaifengChina
  4. 4.School of Information Science and TechnologyJiujiang UniversityJiujiangChina
  5. 5.School of InformationGuizhou University of Finance and EconomicsGuiyangChina

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