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Communication Dans Un Congrès Année : 2020

Pedestrian detection and classification for autonomous train

Résumé

In this paper, we present a combined approach for human localization and classification in Autonomous Train application. Our contribution is threefold. (a) The creation of a new dataset for workers wearing orange vests in a railway environment context. (b) A deep learning supervised YOLO object detector for persons detection combined with a linear SVM (Support Vector Machine) classifier for persons classification into workers wearing orange vests or travelers. (c) A realtime vision-based technique for the environment monitoring in a driverless train application. Experimental results evaluate the parameters of our two stages detection approach and show that our algorithm is robust in detecting and classifying railway workers for a real-time implementation on an embedded system. Our implementation on an embedded system allows a detection with a correct classification rate of 98.5 % of accuracy and a classification time of 1 ms per frame.
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Dates et versions

hal-03138789 , version 1 (11-02-2021)

Identifiants

Citer

Ankur Mahtani, Wael Ben Messaoud, Abdelmalik Taleb-Ahmed, Smail Niar, Clément Strauss. Pedestrian detection and classification for autonomous train. IEEE 4th International Conference on Image Processing, Applications and Systems, IPAS 2020, Dec 2020, Genova, Italy. pp.52-57, ⟨10.1109/IPAS50080.2020.9334938⟩. ⟨hal-03138789⟩
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