An UNet-Based Head Shoulder Segmentation Network

Hong Xia Xie, Chih Yang Lin, Hua Zheng, Pei Yu Lin

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

10 Scopus citations

Abstract

Within the rapidly developing field of computer vision, pedestrian detection is a fundamental and challenging task for both industry and academia. However, object segmentation information can help the network to capture the attention of the model during training. In this paper, we propose a head-shoulder segmentation network based on modified U-Net network. The architecture consists of a contracting path to capture information from a lower layer and a symmetric expanding path to enable precise localization. The proposed model aims to effectively segment the head-shoulder portion of pedestrian without a huge annotated training sample. Segmentation of a random image takes less than a second on NVIDIA GTX 1070. This paper will show the mean IOU and some segmentation results to prove effectiveness of this model.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781538663011
DOIs
StatePublished - 27 Aug 2018
Event5th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018 - Taichung, Taiwan
Duration: 19 May 201821 May 2018

Publication series

Name2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018

Conference

Conference5th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018
Country/TerritoryTaiwan
CityTaichung
Period19/05/1821/05/18

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