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A Survey on Pedestrian Detection: Towards Integrating Vulnerable Road Users into Sensor Networks

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Real-time and Autonomous Systems 2022 (Real-Time 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 674))

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Abstract

This paper provides an overview of fundamental and recent 2D and 3D pedestrian detection methods. It is part of the ongoing investigation on integrating vulnerable road users into sensor networks. Besides the sensor-specific object detection methods based on LIDAR sensors, RADAR sensors, thermal imaging cameras, RGBD cameras, and RGB cameras, a selection of sensor fusion methods is presented. Shown methods have been developed to increase traffic safety for vulnerable road users, which include pedestrians and cyclists.

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Authors and Affiliations

  1. Department of Computer Science, Hamburg University of Applied Sciences, Berliner Tor 7, 20099, Hamburg, Germany

    Maximilian De Muirier, Stephan Pareigis & Tim Tiedemann

Authors
  1. Maximilian De Muirier
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  2. Stephan Pareigis
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  3. Tim Tiedemann
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Correspondence to Maximilian De Muirier .

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Editors and Affiliations

  1. Lehrgebiet Kommunikationsnetze, FernUniversität in Hagen, Hagen, Germany

    Herwig Unger

  2. Lehrgebiet Kommunikationsnetze, FernUniversität in Hagen, Hagen, Germany

    Marcel Schaible

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De Muirier, M., Pareigis, S., Tiedemann, T. (2023). A Survey on Pedestrian Detection: Towards Integrating Vulnerable Road Users into Sensor Networks. In: Unger, H., Schaible, M. (eds) Real-time and Autonomous Systems 2022. Real-Time 2022. Lecture Notes in Networks and Systems, vol 674. Springer, Cham. https://doi.org/10.1007/978-3-031-32700-1_10

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