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Experiment Analysis on Fast Features Bag Approach for Pedestrian Re-recognition

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Exploration of Novel Intelligent Optimization Algorithms (ISICA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1590))

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Abstract

Pedestrian re-recognition is a significant and challenging research topic in the field of multi-camera surveillance in smart transportation. This paper defines the important practical significance to improve the intelligent monitoring system. The feature representation and feature matching are two challenging factors in pedestrian re-recognition. In this paper, we improved the traditional bag of features algorithm due to its shortcomings, such as low classification accuracy and low computational efficiency. The main work of this paper is described as follows; First, we proposed the fast feature bag (FFB) method by using the SURF algorithm, which extracts the initial features descriptor and constructs a visual dictionary to handle with the influence factors e.g., light changes and scale-invariant. After that, we adopted the covariance descriptor method to the pedestrian re-recognition algorithm, which improves the matching accuracy in the few samples cases. Then, we used the LIBSVM classifier based on the FFB algorithm to improve the efficiency of the pedestrian re-recognition algorithm. By contrast with the CMC curve, we compared the proposed method with traditional mainstream algorithms by using pedestrian re-recognition datasets to prove that it is effective to solve the complex pedestrian re-recognition problems and perform better than the traditional methods in terms of matching efficiency and classification accuracy.

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Acknowledgments

This work is supported by the Key Field Special Project of Guangdong Provincial Department of Education with No.2021ZDZX1029.

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Authors and Affiliations

  1. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, Guangdong, China

    Hassan Jalil & Kangshun Li

  2. School of Computer and Information, Dongguan City University, Dongguan, 523808, Guangdong, China

    Kangshun Li

Authors
  1. Hassan Jalil
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  2. Kangshun Li
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Corresponding author

Correspondence to Kangshun Li .

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Editors and Affiliations

  1. South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. The University of Aizu, Aizu-Wakamatsu, Japan

    Yong Liu

  3. South China Agricultural University, Guangzhou, China

    Wenxiang Wang

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Jalil, H., Li, K. (2022). Experiment Analysis on Fast Features Bag Approach for Pedestrian Re-recognition. In: Li, K., Liu, Y., Wang, W. (eds) Exploration of Novel Intelligent Optimization Algorithms. ISICA 2021. Communications in Computer and Information Science, vol 1590. Springer, Singapore. https://doi.org/10.1007/978-981-19-4109-2_14

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  • DOI: https://doi.org/10.1007/978-981-19-4109-2_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-4108-5

  • Online ISBN: 978-981-19-4109-2

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