Incorporation of Neural Network to HPMHT for Tracking Multiple Targets

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TÜRKMEN I., ÇELİK H.

ELEKTRONIKA IR ELEKTROTECHNIKA, vol.21, no.4, pp.3-6, 2015 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 21 Issue: 4
  • Publication Date: 2015
  • Doi Number: 10.5755/j01.eee.21.4.12772
  • Journal Name: ELEKTRONIKA IR ELEKTROTECHNIKA
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.3-6
  • Erciyes University Affiliated: Yes

Abstract

In this paper, a hybrid method which combines homothetic multi-hypothesis tracker (HPMHT) and artificial neural networks (ANNs) is presented to solve multiple target tracking problem. The performances of the proposed neural network aided homothetic multi-hypothesis tracker (NNAHPMHT) and the HPMHT are compared for two different test scenarios. It was observed that the estimation performances obtained from the NNAHPMHT are better than those obtained from only the HPMHT. The NNAHPMHT method doesn't require additional complex modeling for tracking multiple targets. The additional implementation time originated from NNAHPMHT is only recall time of the ANN. For this reason, the proposed method is very suitable for real-time implementation.