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Link to original content: https://doi.org/10.1007/978-3-031-23236-7_25
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Long-Term Person Reidentification: Challenges and Outlook

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Optimization, Learning Algorithms and Applications (OL2A 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1754))

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Abstract

Person reidentification, i.e., retrieving a person of interest across several non-overlapping cameras, is a task that is far from trivial. Despite its great commercial value and wide range of applications (e.g., surveillance, intelligent environments, forensics, service robotics, marketing), it remains unsolved, even when the individuals do not change clothes during the recognition period. This paper provides an outlook on long-term person reidentification, an emerging research topic regarding when consecutive acquisitions of an individual can be found apart for days or even months, making such a task even more challenging. A long-term reidentification system using face recognition is presented to emphasize current techniques’ limitations.

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Acknowledgements

This work has been financially supported by national grant from the FCT (Fundação para a Ciência e a Tecnologia), under the project UIDB/05567/2020. Diego Haddad would like to thank the Brazillian organization National Council for Scientific and Technological Development (CNPq) for its financial support.

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Authors and Affiliations

  1. Celso Suckow da Fonseca Federal Center of Technological Education, Rio de Janeiro, Brazil

    Anderson Manhães, Gabriel Matos, Milena F. Pinto, Jeferson Colares, Diego Brandão & Diego Haddad

  2. Smart Cities Research Center, Polytechnic Institute of Tomar, Tomar, Portugal

    Douglas O. Cardoso

  3. Research Centre in Digitalization and Intelligent Robotics, Polytechnic Institute of Bragança, Bragança, Portugal

    Diego Brandão

  4. Research Center in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253, Bragança, Portugal

    Paulo Leitão

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  1. Anderson Manhães
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  2. Gabriel Matos
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  4. Milena F. Pinto
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  6. Paulo Leitão
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  7. Diego Brandão
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  8. Diego Haddad
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Corresponding author

Correspondence to Diego Haddad .

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Editors and Affiliations

  1. Instituto Politécnico de Bragança, Bragança, Portugal

    Ana I. Pereira

  2. University of Ljubljana, Ljubljana, Slovenia

    Andrej Košir

  3. Instituto Politécnico de Bragança, Bragança, Portugal

    Florbela P. Fernandes

  4. Instituto Politécnico de Bragança, Bragança, Portugal

    Maria F. Pacheco

  5. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Teixeira

  6. Instituto Politécnico de Bragança, Bragança, Portugal

    Rui P. Lopes

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Manhães, A. et al. (2022). Long-Term Person Reidentification: Challenges and Outlook. In: Pereira, A.I., Košir, A., Fernandes, F.P., Pacheco, M.F., Teixeira, J.P., Lopes, R.P. (eds) Optimization, Learning Algorithms and Applications. OL2A 2022. Communications in Computer and Information Science, vol 1754. Springer, Cham. https://doi.org/10.1007/978-3-031-23236-7_25

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  • DOI: https://doi.org/10.1007/978-3-031-23236-7_25

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