Pedestrian Detection in Aerial Images Using Vanishing Point Transformation and Deep Learning

Ya Ching Chang, Hua Tsung Chen, Jen-Hui Chuang, I. Chun Liao

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

5 Scopus citations

Abstract

Drones are well-liked nowadays. However, deep learning models for object detection still cannot have high detection rates for pedestrians in aerial images even though they already show high precision on PASCAL VOC 2007. The main challenges of aerial image analysis include: (i) the size of an object in aerial images can be very small, and (ii) the objects in aerial images are tilted outward due to perspective projection deformation, which make the pedestrians hard to recognize in aerial images. In this paper, we utilize image partition and vanishing point transformation to overcome the above challenges. Experimental results demonstrate that such pre-processing methods can indeed increase the detection rates significantly for some deep learning models.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PublisherIEEE Computer Society
Pages1917-1921
Number of pages5
ISBN (Electronic)9781479970612
DOIs
StatePublished - 29 Aug 2018
Event25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece
Duration: 7 Oct 201810 Oct 2018

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference25th IEEE International Conference on Image Processing, ICIP 2018
CountryGreece
CityAthens
Period7/10/1810/10/18

Keywords

  • Aerial image
  • Deep learning
  • Drone
  • Pedestrian detection
  • Vanishing point transformation

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