IEIE Transactions on Smart Processing and Computing

The Institute of Electronics and Information Engineers (대한전자공학회)
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Evaluation of Recurrent Neural Network Variants for Person Re-identification

  • Le, Cuong Vo (School of Electronics and Telecommunications, Hanoi University of Science and Technology) ;
  • Tuan, Nghia Nguyen (School of Electronics and Telecommunications, Hanoi University of Science and Technology) ;
  • Hong, Quan Nguyen (School of Electronics and Telecommunications, Hanoi University of Science and Technology) ;
  • Lee, Hyuk-Jae (Inter-university Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University)
  • Received : 2017.05.03
  • Accepted : 2017.05.26
  • Published : 2017.06.30
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Abstract

Instead of using only spatial features from a single frame for person re-identification, a combination of spatial and temporal factors boosts the performance of the system. A recurrent neural network (RNN) shows its effectiveness in generating highly discriminative sequence-level human representations. In this work, we implement RNN, three Long Short Term Memory (LSTM) network variants, and Gated Recurrent Unit (GRU) on Caffe deep learning framework, and we then conduct experiments to compare performance in terms of size and accuracy for person re-identification. We propose using GRU for the optimized choice as the experimental results show that the GRU achieves the highest accuracy despite having fewer parameters than the others.

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References

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