Pedestrian Detection In Image By Machine Learning

Tilgner, Martin

Pedestrian Detection In Image By Machine Learning

Files

409_eeict2019.pdf(1.36 MB)

Date

2019

Authors

Tilgner, Martin

Publisher

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

Abstract

This work deals with pedestrian detection via convolutional neural network which can be used in autonomous car driving systems to improve travel safety. The work focuses on the influence of the training dataset on the resulting network behavior. The Faster R-CNN with ResNet 101 as backbone network and the SSDLite with MobileNet v2 as backbone network meta-architectures were selected for parameter testing. Both networks achieved real-time detection while accuracy was 61.92 % for the Faster R-CNN and 31.72 % for the SSDLite.

Keywords

Object detection, Convolutional Neural Network, Machine learning, Faster R-CNN, SSDLite

Citation

Proceedings of the 25st Conference STUDENT EEICT 2019. s. 409-412. ISBN 978-80-214-5735-5
http://www.feec.vutbr.cz/EEICT/

Document type

Peer-reviewed

Document version

Published version

Language of document

cs

Document licence