@inproceedings{1882273f6da34867ab9e27af8a07fdeb,
title = "Overhead View Person Detection Using YOLO",
abstract = "In video surveillance system, one of the important task is to detect person. In recent years, different computer vision and deep learning algorithms have been developed, which provides robust person detection results. Majority of these developed techniques focused on frontal and asymmetric views. Therefore, in this paper, person detection has been performed from a significantly changed perspective i.e. overhead view. A deep learning model i.e. YOLO (You Look Only Once) has been explored in the context of person detection from overhead view. The model is trained on frontal view data set and tested on overhead view person data set. Furthermore, overhead view person counting has been performed using information of classified bounding box. The YOLO model produces significantly good results with TPR of 95% and FPR up to 0.2%.",
keywords = "Deep Learning, Overhead view, Person Counting, Person detection, YOLO",
author = "Misbah Ahmad and Imran Ahmed and Awais Adnan",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 10th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2019 ; Conference date: 10-10-2019 Through 12-10-2019",
year = "2019",
month = oct,
doi = "10.1109/UEMCON47517.2019.8992980",
language = "English",
series = "2019 IEEE 10th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "627--633",
editor = "Satyajit Chakrabarti and Saha, {Himadri Nath}",
booktitle = "2019 IEEE 10th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2019",
address = "United States",
}