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A Siamese Neural Network for Non-Invasive Baggage Re-Identification.

Pier Luigi Mazzeo1, Christian Libetta2, Paolo Spagnolo1

  • 1Institute of Applied Sciences and Intelligent Systems-CNR, Via Monteroni sn, 73100 Lecce, Italy.

Journal of Imaging
|August 30, 2021
PubMed
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This study introduces a Siamese Neural Network model for baggage re-identification, improving airport baggage handling safety and speed. The model accurately estimates suitcase similarity from images, outperforming existing methods.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Transportation Systems

Background:

  • Baggage handling systems (BHS) face challenges with traffic jams caused by incorrect check-in entries.
  • Efficient baggage re-identification is crucial for safer and faster airport operations.

Purpose of the Study:

  • To develop a Siamese Neural Network model for accurate baggage similarity estimation.
  • To enhance the re-identification capabilities within airport Baggage Handling Systems (BHS).

Main Methods:

  • A Siamese Neural Network architecture was employed to learn discriminative features for image similarity.
  • The model was trained on a publicly available suitcase dataset, allowing for varied image conditions.
  • The model's performance was evaluated against state-of-the-art architectures.
Keywords:
Siamese Neural Networksbaggage re-identificationdeep learning

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Main Results:

  • The proposed Siamese Neural Network model achieved high accuracy in estimating baggage similarity.
  • The model demonstrated superior performance compared to the leading state-of-the-art architecture on the benchmark dataset.
  • The network effectively learns features for robust re-identification across different image conditions.

Conclusions:

  • The developed Siamese Neural Network model offers a promising solution for improving baggage re-identification in BHS.
  • This approach enhances operational efficiency and safety in airport baggage handling processes.
  • The model's adaptability to different pre-trained backbones suggests broad applicability.