Pseudo Ground Truth Segmentation Mask to Improve Video Prediction Quality

Mu Chien Hsu, Jui Chun Shyur, Hiroshi Watanabe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Video prediction to foresee future events is an extremely difficult job since it involves spatial feature extraction and temporal sequential analysis. We identified that the semantic information is actually crucial to prediction, and proposed using 'pseudo ground truth' segmentation masks which are generated automatically in real time and add them to the input layers as extra information to predict future frames. Experiments conducted on our self-defined network demonstrated drastically higher quality predictions are achieved when compared with other state-of-the-art direct video prediction models.

Original languageEnglish
Title of host publication2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages831-832
Number of pages2
ISBN (Electronic)9781728198026
DOIs
Publication statusPublished - 2020 Oct 13
Event9th IEEE Global Conference on Consumer Electronics, GCCE 2020 - Kobe, Japan
Duration: 2020 Oct 132020 Oct 16

Publication series

Name2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020

Conference

Conference9th IEEE Global Conference on Consumer Electronics, GCCE 2020
Country/TerritoryJapan
CityKobe
Period20/10/1320/10/16

Keywords

  • segmentation
  • video prediction

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Media Technology
  • Instrumentation
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition

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