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Master and Rookie Networks for Person Re-identification

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Computer Analysis of Images and Patterns (CAIP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11679))

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Abstract

Recognizing different visual signatures of people across non-overlapping cameras is still an open problem of great interest for the computer vision community, especially due to its importance in automatic video surveillance on large-scale environments. A main aspect of this application field, known as person re-identification (re-id), is the feature extraction step used to define a robust appearance of a person. In this paper, a novel two-branch Convolutional Neural Network (CNN) architecture for person re-id in video sequences is proposed. A pre-trained branch, called Master, leads the learning phase of the other un-trained branch, called Rookie. Using this strategy, the Rookie network is able to learn complementary features with respect to those computed by the Master network, thus obtaining a more discriminative model. Extensive experiments on two popular challenging re-id datasets have shown increasing performance in terms of convergence speed as well as accuracy in comparison to standard models, thus providing an alternative and concrete contribution to the current re-id state-of-the-art.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Sapienza University, Via Salaria 113, 00198, Rome, Italy

    Danilo Avola, Marco Cascio, Luigi Cinque, Alessio Fagioli & Cristiano Massaroni

  2. Department of Mathematics, Computer Science and Physics, University of Udine, Via delle Scienze 206, 33100, Udine, Italy

    Gian Luca Foresti

Authors
  1. Danilo Avola
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  2. Marco Cascio
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  3. Luigi Cinque
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  4. Alessio Fagioli
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  5. Gian Luca Foresti
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  6. Cristiano Massaroni
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Corresponding author

Correspondence to Cristiano Massaroni .

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Editors and Affiliations

  1. Department of Computer and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano (SA), Italy

    Mario Vento

  2. Department of Computer and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano (SA), Italy

    Gennaro Percannella

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Avola, D., Cascio, M., Cinque, L., Fagioli, A., Foresti, G.L., Massaroni, C. (2019). Master and Rookie Networks for Person Re-identification. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11679. Springer, Cham. https://doi.org/10.1007/978-3-030-29891-3_41

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  • DOI: https://doi.org/10.1007/978-3-030-29891-3_41

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