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Towards a Novel Reidentification Method Using Metaheuristics

  • Tarik LjouadEmail author
  • Aouatif Amine
  • Ayoub Al-Hamadi
  • Mohammed Rziza
Chapter
  • 631 Downloads
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 62)

Abstract

Tracking multiple moving objects in a video sequence can be formulated as a profile matching problem. Reidentifying a profile within a crowd is done by a matching process between the tracked person and the different moving individuals within the same frame. In that context, the feature matching task can be approximated to a search for the profile that maximizes a considered similarity measure. In this work, we introduce a novel Modified Cuckoo Search (MCS) based reidentification algorithm. A complex descriptor representing each moving person is built from different low level visual features such as the color and the texture components. We make use of a database that involves all previously detected descriptors, forming therefore a discrete search space where the sought solution is a descriptor and its quality is represented by its similarity to the query profile. The approach is evaluated within a multiple object tracking scenario, and a validation process using the normalized cross correlation method to accept or reject the obtained reidentification results is included. The experimental results show promising performances in terms of computation cost as well as reidentification rate.

Keywords

Object tracking Cuckoo search 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Tarik Ljouad
    • 1
    Email author
  • Aouatif Amine
    • 2
  • Ayoub Al-Hamadi
    • 3
  • Mohammed Rziza
    • 1
  1. 1.Faculty of Sciences of Rabat (FSR)Mohammed V UniversityRabatMorocco
  2. 2.LGS Laboratory, National School of Applied Sciences (ENSA)Ibn Tofail UniversityKenitraMorocco
  3. 3.Institute for Information Technology and Communications (IIKT)Otto-von-Guericke-University MagdeburgMagdeburgGermany

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