Novel Architecture for Human Re-Identification with a Two-Stream Neural Network and Attention Mechanism

Authors

  • Babak Rahi Department of Electronics and Computer Engineering, Brunel University London, Uxbridge, UK
  • Man Qi School of Engineering, Technology and Design, Canterbury Christ Church University, Canterbury, UK

DOI:

https://doi.org/10.31577/cai_2022_4_905

Keywords:

Identification of persons, multi-layer neural network, gait recognition, human re-identification, convolutional neural networks, attention mechanism

Abstract

This paper proposes a novel architecture that utilises an attention mechanism in conjunction with multi-stream convolutional neural networks (CNN) to obtain high accuracy in human re-identification (Reid). The proposed architecture consists of four blocks. First, the pre-processing block prepares the input data and feeds it into a spatial-temporal two-stream CNN (STC) with two fusion points that extract the spatial-temporal features. Next, the spatial-temporal attentional LSTM block (STA) automatically fine-tunes the extracted features and assigns weight to the more critical frames in the video sequence by using an attention mechanism. Extensive experiments on four of the most popular datasets support our architecture. Finally, the results are compared with the state of the art, which shows the superiority of this approach.

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Published

2022-11-09

How to Cite

Rahi, B., & Qi, M. (2022). Novel Architecture for Human Re-Identification with a Two-Stream Neural Network and Attention Mechanism. COMPUTING AND INFORMATICS, 41(4), 905–930. https://doi.org/10.31577/cai_2022_4_905

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