A Pedestrian Detection Case Study for a Traffic Light Controller

Alexander Wendt; Horst Possegger; Matthias Bittner; Daniel Schnöll; Matthias Wess; Dušan Malić; Horst Bischof; Axel Jantsch

doi:10.1007/978-3-031-39932-9_4

A Pedestrian Detection Case Study for a Traffic Light Controller

Alexander Wendt, Horst Possegger, Matthias Bittner, Daniel Schnöll, Matthias Wess, Dušan Malić, Horst Bischof, Axel Jantsch^*

^*Corresponding author for this work

Institute of Visual Computing (7100)

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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.

Original language	English
Title of host publication	Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing
Subtitle of host publication	Software Optimazations and Hardware/Software Codesign
Editors	Sudeep Pasricha, Muhammad Shafique
Place of Publication	Cham
Publisher	Springer
Pages	75-96
ISBN (Print)	978-3-031-39931-2
DOIs	https://doi.org/10.1007/978-3-031-39932-9_4
Publication status	Published - 2023

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Wendt, A., Possegger, H., Bittner, M., Schnöll, D., Wess, M., Malić, D., Bischof, H., & Jantsch, A. (2023). A Pedestrian Detection Case Study for a Traffic Light Controller. In S. Pasricha, & M. Shafique (Eds.), Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing: Software Optimazations and Hardware/Software Codesign (pp. 75-96). Springer. https://doi.org/10.1007/978-3-031-39932-9_4

A Pedestrian Detection Case Study for a Traffic Light Controller. / Wendt, Alexander; Possegger, Horst; Bittner, Matthias et al.
Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing: Software Optimazations and Hardware/Software Codesign. ed. / Sudeep Pasricha; Muhammad Shafique. Cham: Springer, 2023. p. 75-96.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Wendt, A, Possegger, H, Bittner, M, Schnöll, D, Wess, M, Malić, D , Bischof, H & Jantsch, A 2023, A Pedestrian Detection Case Study for a Traffic Light Controller. in S Pasricha & M Shafique (eds), Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing: Software Optimazations and Hardware/Software Codesign. Springer, Cham, pp. 75-96. https://doi.org/10.1007/978-3-031-39932-9_4

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A Pedestrian Detection Case Study for a Traffic Light Controller

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