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Pedestrian Detection Model Algorithm Optimization Based on Deep Convolutional Neural Network

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Proceedings of the 5th International Symposium for Intelligent Transportation and Smart City (ITASC) (ITASC 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1042))

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Abstract

Pedestrian detection is a classical problem in computer vision and has been a difficult problem to solve for a long time. Compared with face detection, it is very difficult to accurately detect pedestrians in various scenarios because of the complex posture of human body, larger deformation, and more serious problems such as attachment and occlusion. This paper focuses on the typical pedestrian detection model - YOLO model. Through experiments, the principle of pedestrian detection model algorithm and its model effect are studied to solve the difficulties in pedestrian detection. In YOLO, logistic regression is used to predict the object score of each boundary box, and multi-scale fusion is used to make prediction. By observing the mAP index, it is concluded that the YOLO algorithm has a good effect on single-label pedestrian detection, and the calculation efficiency is high.

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Acknowledgments

The project is supported by Science and Technology Commission of Shanghai Municipality (Number 22dz1208505, 20DZ1202900, 19DZ1204200).

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Authors and Affiliations

  1. State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Yizeng Wang & Hu Hao

  2. Department of Computer Science, City University of Hong Kong, Hong Kong, 999077, China

    Yizeng Wang

  3. The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, No. 4800 Cao’an Road, Shanghai, 201804, China

    **aoqing Zeng

  4. Shanghai Municipal Engineering Construction and Development Co., Ltd., Shanghai, China

    Dongliang Feng

Authors
  1. Yizeng Wang
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  2. Hu Hao
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  3. **aoqing Zeng
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  4. Dongliang Feng
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Corresponding author

Correspondence to Dongliang Feng .

Editor information

Editors and Affiliations

  1. College of Transportation Engineering, Tongji University, Shanghai, China

    **aoqing Zeng

  2. College of Civil Engineering, Tongji University, Shanghai, Shanghai, China

    **ongyao **e

  3. College of Future Transportation, Chang'an University, **'an, Shaanxi, China

    Jian Sun

  4. College of Environmental Science and Engineering, Tongji University, Shanghai, Shanghai, China

    Limin Ma

  5. School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA

    Yinong Chen

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Wang, Y., Hao, H., Zeng, X., Feng, D. (2023). Pedestrian Detection Model Algorithm Optimization Based on Deep Convolutional Neural Network. In: Zeng, X., **e, X., Sun, J., Ma, L., Chen, Y. (eds) Proceedings of the 5th International Symposium for Intelligent Transportation and Smart City (ITASC). ITASC 2022. Lecture Notes in Electrical Engineering, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-99-2252-9_2

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  • DOI: https://doi.org/10.1007/978-981-99-2252-9_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-2251-2

  • Online ISBN: 978-981-99-2252-9

  • eBook Packages: EngineeringEngineering (R0)

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