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Deep CNN-based Pedestrian Detection for Intelligent Infrastructure

Abstract : Autonomous driving systems and driver assistance systems are becoming the center of attention in transport technology. Given its safety criticality, pedestrian detection is a highly important task. Transport oriented intelligent systems use embedded sensors for the detection task. However, vehicle side detection is starting to show its limitations especially when dealing with certain challenges such as occlusions. In this paper, we propose an infrastructure side perception system that has a bird's eye view. We introduce a new deep pedestrian detector that can use the detection results to warn nearby vehicles of the presence of pedestrians on the road. The results show that our proposed system is able to detect pedestrians in most conditions with 70.41% precision and 69.17% recall.
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https://hal-uphf.archives-ouvertes.fr/hal-03566199
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Submitted on : Friday, February 11, 2022 - 2:04:51 PM
Last modification on : Wednesday, March 23, 2022 - 3:51:16 PM

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Bilel Tarchoun, Imen Jegham, Anouar Ben Khalifa, Ihsen Alouani, Mohamed Ali Mahjoub. Deep CNN-based Pedestrian Detection for Intelligent Infrastructure. 2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Sep 2020, Sousse, Tunisia. pp.1-6, ⟨10.1109/ATSIP49331.2020.9231712⟩. ⟨hal-03566199⟩

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