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A Survey on Pedestrian Detection System Using Computer Vision and Deep Learning

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Advanced Computational Paradigms and Hybrid Intelligent Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1373))

Abstract

An important research topic in computer vision is pedestrian detection. Research in this topic has wide applications using deep learning methods that are capable of learning high-level representation of features necessary for automatic detection of the pedestrians. The objective of this paper is to study various systems based on vision-based pedestrian detection using deep learning frameworks. The paper considers the performance of some methods over the dataset Caltech, INRIA and KITTI in the span of study. In the extensive study, it has been found out ExtAtt method has gained maximum average precision rate on KITTI dataset, while PCN method has achieved lower log average miss rate on both Caltech and INRIA dataset.

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Authors and Affiliations

  1. Sikkim Manipal Institute of Technology, Majitar, Sikkim, India

    Souvik Pattanayak, Chitrapriya Ningthoujam & Nitisha Pradhan

Authors
  1. Souvik Pattanayak
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  2. Chitrapriya Ningthoujam
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  3. Nitisha Pradhan
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology, New Delhi, Delhi, India

    Tapan Kumar Gandhi

  2. Department of Computer Science and Engineering, SRM University, Amaravati, Andhra Pradesh, India

    Debanjan Konar

  3. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Majitar, Sikkim, India

    Biswaraj Sen

  4. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Majitar, Sikkim, India

    Kalpana Sharma

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Pattanayak, S., Ningthoujam, C., Pradhan, N. (2022). A Survey on Pedestrian Detection System Using Computer Vision and Deep Learning. In: Gandhi, T.K., Konar, D., Sen, B., Sharma, K. (eds) Advanced Computational Paradigms and Hybrid Intelligent Computing . Advances in Intelligent Systems and Computing, vol 1373. Springer, Singapore. https://doi.org/10.1007/978-981-16-4369-9_41

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