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Person Re-Identification Using Pose-Driven Body Parts

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Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.1 (SETIT 2018)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 146))

Abstract

The topic of Person Re-Identification (Re-ID) is currently attracting much interest from researchers due to the various possible applications such as behavior recognition, person tracking and safety purposes at public places. General approach is to extract discriminative color and texture features from images and calculate their distances as a measure of similarity. Most of the work consider whole body to extract descriptors. However, human body maybe occluded or seen from different views that prevent correct matching between persons.

We propose in this paper to use a reliable pose estimation algorithm to extract meaningful body parts. Then, we extract descriptors from each part separately using LOcal Maximal Occurrence (LOMO) algorithm and Cross-view Quadratic Discriminant Analysis (XQDA) metric learning algorithm to compute the similarity. A comparison between state-of-the-art Re-ID methods in most commonly used benchmark Re-ID datasets will be also presented in this work.

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Author information

Authors and Affiliations

  1. University of Gabes, National Engineering School of Gabes, Gabes, Tunisia

    Salwa Baabou

  2. Pattern Analysis and Computer Vision (PAVIS), Italian Institute of Technology, Genoa, Italy

    Behzad Mirmahboub

  3. INRIA Sophia Antipolis Mediterranee, Biot, France

    François Bremond

  4. University of Sfax, National Engineering School of Sfax Laboratory of Electronics and Information Technology (LETI), Sfax, Tunisia

    Salwa Baabou, Mohamed Amine Farah & Abdennaceur Kachouri

Authors
  1. Salwa Baabou
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  2. Behzad Mirmahboub
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  3. François Bremond
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  4. Mohamed Amine Farah
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  5. Abdennaceur Kachouri
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Corresponding author

Correspondence to Salwa Baabou .

Editor information

Editors and Affiliations

  1. SETIT Lab, University of Sfax, Sfax, Tunisia

    Med Salim Bouhlel

  2. DIBRIS - University of Genoa, Genova, Genova, Italy

    Stefano Rovetta

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Baabou, S., Mirmahboub, B., Bremond, F., Farah, M.A., Kachouri, A. (2020). Person Re-Identification Using Pose-Driven Body Parts. In: Bouhlel, M., Rovetta, S. (eds) Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.1. SETIT 2018. Smart Innovation, Systems and Technologies, vol 146. Springer, Cham. https://doi.org/10.1007/978-3-030-21005-2_29

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