Deep Background Subtraction of Thermal and Visible Imagery for Pedestrian Detection in Videos

Yijun Yan, Huimin Zhao, Fu Jen Kao, Valentin Masero Vargas, Sophia Zhao, Jinchang Ren*

*Corresponding author for this work

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

8 Scopus citations

Abstract

In this paper, we introduce an efficient framework to subtract the background from both visible and thermal imagery for pedestrians’ detection in the urban scene. We use a deep neural network (DNN) to train the background subtraction model. For the training of the DNN, we first generate an initial background map and then employ randomly 5% video frames, background map, and manually segmented ground truth. Then we apply a cognition-based post-processing to further smooth the foreground detection result. We evaluate our method against our previous work and 11 recently widely cited method on three challenge video series selected from a publicly available color-thermal benchmark dataset OCTBVS. Promising results have been shown that the proposed DNN-based approach can successfully detect the pedestrians with good shape in most scenes regardless of illuminate changes and occlusion problem.

Original languageEnglish
Title of host publicationAdvances in Brain Inspired Cognitive Systems - 9th International Conference, BICS 2018, Proceedings
EditorsAmir Hussain, Bin Luo, Jiangbin Zheng, Xinbo Zhao, Cheng-Lin Liu, Jinchang Ren, Huimin Zhao
PublisherSpringer Verlag
Pages75-84
Number of pages10
ISBN (Print)9783030005627
DOIs
StatePublished - 2018
Event9th International Conference on Brain-Inspired Cognitive Systems, BICS 2018 - Xi'an, China
Duration: 7 Jul 20188 Jul 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10989 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Brain-Inspired Cognitive Systems, BICS 2018
Country/TerritoryChina
CityXi'an
Period7/07/188/07/18

Keywords

  • Deep neural network (DNN)
  • Pedestrian detection
  • Video salient objects

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