Traffic control gesture recognition for autonomous vehicles
Wiederer J, Bouazizi A, Kressel U, Belagiannis V (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 10676-10683
Conference Proceedings Title: IEEE International Conference on Intelligent Robots and Systems
Event location: Las Vegas, NV, USA
ISBN: 9781728162126
DOI: 10.1109/IROS45743.2020.9341214
Abstract
A car driver knows how to react on the gestures of the traffic officers. Clearly, this is not the case for the autonomous vehicle, unless it has road traffic control gesture recognition functionalities. In this work, we address the limitation of the existing autonomous driving datasets to provide learning data for traffic control gesture recognition. We introduce a dataset that is based on 3D body skeleton input to perform traffic control gesture classification on every time step. Our dataset consists of 250 sequences from several actors, ranging from 16 to 90 seconds per sequence. To evaluate our dataset, we propose eight sequential processing models based on deep neural networks such as recurrent networks, attention mechanism, temporal convolutional networks and graph convolutional networks. We present an extensive evaluation and analysis of all approaches for our dataset, as well as real-world quantitative evaluation. The code and dataset is publicly available4.
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Germany (DE)How to cite
APA:
Wiederer, J., Bouazizi, A., Kressel, U., & Belagiannis, V. (2020). Traffic control gesture recognition for autonomous vehicles. In IEEE International Conference on Intelligent Robots and Systems (pp. 10676-10683). Las Vegas, NV, USA: Institute of Electrical and Electronics Engineers Inc..
MLA:
Wiederer, Julian, et al. "Traffic control gesture recognition for autonomous vehicles." Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020, Las Vegas, NV, USA Institute of Electrical and Electronics Engineers Inc., 2020. 10676-10683.
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