@inproceedings{76f82d3f944a4a55bc16996a2419a783,
title = "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. ",
keywords = "Faster R-CNN, intelligent infrastructure, Intelligent transportation systems, Pedestrian detection, transfer learning",
author = "Bilel Tarchoun and Imen Jegham and Khalifa, \{Anouar Ben\} and Ihsen Alouani and Mahjoub, \{Mohamed Ali\}",
year = "2020",
month = oct,
day = "20",
doi = "10.1109/ATSIP49331.2020.9231712",
language = "English",
isbn = "9781728175140",
series = "International Conference on Advanced Technologies for Signal and Image Processing: Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings of the 5th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2020",
address = "United States",
note = "5th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP ; Conference date: 02-09-2020 Through 05-09-2020",
}