Overhead View Person Detection Using YOLO

Misbah Ahmad, Imran Ahmed, Awais Adnan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

29 Citations (Scopus)

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%.

Original languageEnglish
Title of host publication2019 IEEE 10th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2019
EditorsSatyajit Chakrabarti, Himadri Nath Saha
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages627-633
Number of pages7
ISBN (Electronic)9781728138855
DOIs
Publication statusPublished - Oct 2019
Externally publishedYes
Event10th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2019 - New York City, United States
Duration: 10 Oct 201912 Oct 2019

Publication series

Name2019 IEEE 10th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2019

Conference

Conference10th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2019
Country/TerritoryUnited States
CityNew York City
Period10/10/1912/10/19

Keywords

  • Deep Learning
  • Overhead view
  • Person Counting
  • Person detection
  • YOLO

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