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Using latent features for short-term person re-identification with RGB-D cameras

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Abstract

This paper presents a system for people re-identification in uncontrolled scenarios using RGB-depth cameras. Compared to conventional RGB cameras, the use of depth information greatly simplifies the tasks of segmentation and tracking. In a previous work, we proposed a similar architecture where people were characterized using color-based descriptors that we named bodyprints. In this work, we propose the use of latent feature models to extract more relevant information from the bodyprint descriptors by reducing their dimensionality. Latent features can also cope with missing data in case of occlusions. Different probabilistic latent feature models, such as probabilistic principal component analysis and factor analysis, are compared in the paper. The main difference between the models is how the observation noise is handled in each case. Re-identification experiments have been conducted in a real store where people behaved naturally. The results show that the use of the latent features significantly improves the re-identification rates compared to state-of-the-art works.

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Acknowledgments

The work presented in this paper has been funded by the Spanish Ministry of Science and Technology under the CICYT contract TEVISMART, TEC2009-09146.

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Authors and Affiliations

  1. iTeam - Universitat Politècnica de València, València, Camino de Vera S/N, 46022, Valencia, Spain

    Javier Oliver, Alberto Albiol, Antonio Albiol & José M. Mossi

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  1. Javier Oliver
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  2. Alberto Albiol
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  3. Antonio Albiol
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  4. José M. Mossi
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Correspondence to Alberto Albiol.

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Oliver, J., Albiol, A., Albiol, A. et al. Using latent features for short-term person re-identification with RGB-D cameras. Pattern Anal Applic 19, 549–561 (2016). https://doi.org/10.1007/s10044-015-0489-8

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