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

Salehian, S. and Sebastian, P. and Sayuti, A.B. (2022) Pedestrian Re-identification in Video Surveillance System with Improved Feature Extraction. Lecture Notes in Electrical Engineering, 758. pp. 961-976. ISSN 18761100

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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. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Item Type: Article
Additional Information: cited By 0; Conference of 1st International Conference on Artificial Intelligence for Smart Community, AISC 2020 ; Conference Date: 17 December 2020 Through 18 December 2020; Conference Code:286319
Uncontrolled Keywords: Convolution; Convolutional neural networks; Extraction; Security systems; Statistical tests, Baseline models; Convolutional neural network; Deep convolutional; Features extraction; Input and outputs; Pedestrian; Pedestrian re-identification; Re identifications; Re-id; Video surveillance systems, Feature extraction
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 19 Dec 2023 03:23
Last Modified: 19 Dec 2023 03:23
URI: https://khub.utp.edu.my/scholars/id/eprint/17395

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