Siamese Networks Based People Tracking for 360-degree Videos with Equi-angular Cubemap Format

Kuan Chen Tai, Chih Wei Tang

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

1 Scopus citations

Abstract

This paper proposes a deep learning based pedestrian tracking scheme for 360-degree videos using equiangular cubemap (EAC) format. To be robust against content discontinuity of EAC images, this paper proposes an efficient face stitching scheme such that the tracker keeps tracking across adjacent faces and avoids raising geometric deformation simultaneously. By referring to statistics of score maps from efficient fully-convolutional siamese networks, the proposed mechanism of template update determines the timing of update. Experimental results show that the proposed tracker operates at 60 fps and outperforms the fully convolutional siamese networks based tracker on 360-degree videos with EAC format both in precision plots and success plots.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728173993
DOIs
StatePublished - 28 Sep 2020
Event7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 - Taoyuan, Taiwan
Duration: 28 Sep 202030 Sep 2020

Publication series

Name2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020

Conference

Conference7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
Country/TerritoryTaiwan
CityTaoyuan
Period28/09/2030/09/20

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