Pedestrian Detection based on Deep Fusion Network using Feature Correlation

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

Abstract

Since most of the pedestrian detection method focus on color images, the detection accuracy is lower when the images are captured at night or dark. In this paper, we propose a deep fusion network based pedestrian detection method. We utilize deconvolutional single shot multi-box detector (DSSD) fused at halfway stage. Also, we apply feature correlation for two image modality feature maps to produce a new feature map. For the experiment, we use KAIST dataset to train and test the proposed method. The experiment results show that the proposed method gains 22.46% lower miss rate compared to the KAIST pedestrian detection baseline. In addition, the proposed method shows at least 4.28% lower miss rate compared to the conventional halfway fusion method.

Original languageEnglish
Title of host publication2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages694-699
Number of pages6
ISBN (Electronic)9789881476852
DOIs
StatePublished - 2 Jul 2018
Event10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Honolulu, United States
Duration: 12 Nov 201815 Nov 2018

Publication series

Name2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings

Conference

Conference10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018
Country/TerritoryUnited States
CityHonolulu
Period12/11/1815/11/18

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