Long-term person reidentification: challenges and outlook

Manhães, Anderson; Matos, Gabriel; Cardoso, Douglas O.; Pinto, Milena F.; Colares, Jeferson; Leitão, Paulo; Brandão, Diego; Haddad, Diego

http://hdl.handle.net/10198/27494

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Abstract(s)

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.

Keywords

Long-term person ReID Deep learning Computer vision Multimodal retrieval

URI

http://hdl.handle.net/10198/27494

Citation

Manhães, Anderson; Matos, Gabriel; Cardoso, Douglas O.; Pinto, Milena F.; Colares, Jeferson; Leitão, Paulo; Brandão, Diego; Haddad, Diego (2022). Long-term person reidentification: challenges and outlook. In 2nd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2022. Bragança. 1754, p. 357-372