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A Pedestrian Detection Case Study for a Traffic Light Controller

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Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing

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Abstract

A pedestrian detection system in a traffic light controller is studied. The system is based on Deep Neural Networks (DNNs). We explore several network architectures and hardware platforms to identify the most suitable solution under the given constraints of latency, cost, and precision. Specifically, we study altogether 13 networks from the MobileNet, Yolo, ResNet, and EfficientDet families and 6 platforms based on Nvidia and Intel platforms, conducting 383 experiments. We find that several network-platform combinations meet the given requirements of maximum 100 ms inference latency and 0.9 mean average precision. The most promising are Yolo v5 networks on Nvidia Jetson TX2 and IntelNUC GPU hardware.

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Notes

  1. 1.

    https://images.nvidia.com/aem-dam/en-zz/Solutions/design-visualization/technologies/turing-architecture/NVIDIA-Turing-Architecture-Whitepaper.pdf (accessed: 2022-03-22).

  2. 2.

    https://docs.openvino.ai/latest/index.html (accessed: 2022-03-22).

  3. 3.

    https://developer.nvidia.com/tensorrt (accessed: 2022-03-22).

  4. 4.

    https://developer.nvidia.com/cuda-zone (accessed: 2022-03-22).

  5. 5.

    https://docs.openvino.ai/2021.4/openvino_docs_IE_DG_Device_Plugins.html?highlight=devices (accessed: 2022-03-22).

  6. 6.

    https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-agx-xavier/ (accessed: 2022-02-27).

  7. 7.

    https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-tx2/ (accessed: 2022-02-27).

  8. 8.

    https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-nano (accessed: 2022-02-27).

  9. 9.

    https://www.intel.com/content/www/us/en/developer/tools/neural-compute-stick/overview.html (accessed: 2022-03-04).

  10. 10.

    https://www.intel.com/content/www/us/en/products/details/nuc.html (accessed: 2022-03-04).

  11. 11.

    https://github.com/embedded-machine-learning/scripts-and-guides.

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Author information

Authors and Affiliations

  1. Christian Doppler Laboratory for Embedded Machine Learning, Institute of Computer Technology, TU Wien, Austria

    Alexander Wendt, Matthias Bittner, Daniel Schnöll, Matthias Wess & Axel Jantsch

  2. Christian Doppler Laboratory for Embedded Machine Learning and the Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria

    Horst Possegger, Dušan Malić & Horst Bischof

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  1. Alexander Wendt
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  2. Horst Possegger
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  3. Matthias Bittner
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  4. Daniel Schnöll
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  5. Matthias Wess
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  6. Dušan Malić
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  7. Horst Bischof
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  8. Axel Jantsch
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Correspondence to Axel Jantsch .

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Editors and Affiliations

  1. Colorado State University, Fort Collins, CO, USA

    Sudeep Pasricha

  2. New York University Abu Dhabi, Abu Dhabi, Abu Dhabi, United Arab Emirates

    Muhammad Shafique

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Wendt, A. et al. (2024). A Pedestrian Detection Case Study for a Traffic Light Controller. In: Pasricha, S., Shafique, M. (eds) Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-39932-9_4

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  • DOI: https://doi.org/10.1007/978-3-031-39932-9_4

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  • Online ISBN: 978-3-031-39932-9

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