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Pedestrian Re-identification in Video Surveillance System with Improved Feature Extraction

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International Conference on Artificial Intelligence for Smart Community

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 758))

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Abstract

In this work, we present a comparison between using different pedestrian re-identification (re-id) architectures. We have investigated the advantages of using more complex and deeper convolutional neural networks (CNNs) at the feature extraction stage. The re-id network is based on the summary network presented by (Ahmed and Marks 2015) which we have modified and enhanced. The comparison is done by replacing the feature extraction portion of the network. The newer improved models performed better than the baseline model and resulted in an accuracy of above 96% on our dataset and an accuracy of 92.09% on CUHK03 test dataset. The network takes 2 images as input and, outputs a confidence level indicating whether or not the 2 images depict the same person. The 2 images both go through a CNN with shared weights and the resulting 2 feature maps are used to compare and classify the 2 images as a positive or a negative match.

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Author information

Authors and Affiliations

  1. Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Perak, Malaysia

    Sina Salehian, Patrick Sebastian & Abu Bakar Sayuti

Authors
  1. Sina Salehian
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  2. Patrick Sebastian
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  3. Abu Bakar Sayuti
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Editor information

Editors and Affiliations

  1. Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia

    Rosdiazli Ibrahim

  2. Department of Electrical and Electronics Engineering, Sri Sairam Engineering College, Chennai, India

    K. Porkumaran

  3. Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia

    Ramani Kannan

  4. Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia

    Nursyarizal Mohd Nor

  5. Department of Biomedical Engineering, Sona College of Technology, Salem, India

    S. Prabakar

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Salehian, S., Sebastian, P., Sayuti, A.B. (2022). Pedestrian Re-identification in Video Surveillance System with Improved Feature Extraction. In: Ibrahim, R., K. Porkumaran, Kannan, R., Mohd Nor, N., S. Prabakar (eds) International Conference on Artificial Intelligence for Smart Community. Lecture Notes in Electrical Engineering, vol 758. Springer, Singapore. https://doi.org/10.1007/978-981-16-2183-3_91

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  • DOI: https://doi.org/10.1007/978-981-16-2183-3_91

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-2182-6

  • Online ISBN: 978-981-16-2183-3

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