Methodology for improving detection speed of pedestrians in autonomous vehicle by image class classification

Junkwang Kim, Woo Young Jung, Heechul Jung, Dong Seog Han

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

Abstract

We propose a pedestrian detection method to minimize the amount of computation for classifying and candidate region detection in autonomous vehicles. The minimization of the computational complexity is a crucial factor for commercial products with a limited computational power. In conventional pedestrian detection methods, the number of candidate regions is 300 to 2,000 even if there is no pedestrian in an image. Therefore, the unnecessary computation is significant to classify each falsely decided candidate region. Moreover, it leads to false detection. In this paper, we propose a new methodology for solving this problem, and show through experiments that the processing speed can be improved by the proposed methodology.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Consumer Electronics, ICCE 2018
EditorsSaraju P. Mohanty, Peter Corcoran, Hai Li, Anirban Sengupta, Jong-Hyouk Lee
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-2
Number of pages2
ISBN (Electronic)9781538630259
DOIs
StatePublished - 26 Mar 2018
Event2018 IEEE International Conference on Consumer Electronics, ICCE 2018 - Las Vegas, United States
Duration: 12 Jan 201814 Jan 2018

Publication series

Name2018 IEEE International Conference on Consumer Electronics, ICCE 2018
Volume2018-January

Conference

Conference2018 IEEE International Conference on Consumer Electronics, ICCE 2018
Country/TerritoryUnited States
CityLas Vegas
Period12/01/1814/01/18

Fingerprint

Dive into the research topics of 'Methodology for improving detection speed of pedestrians in autonomous vehicle by image class classification'. Together they form a unique fingerprint.

Cite this