Pedestrian classification and detection in far infrared images

Atmane Khellal, Hongbin Ma*, Qing Fei

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

19 Citations (Scopus)

Abstract

In this paper, a new approach of learning features based on convolutional neural networks for pedestrian detection in far infrared images is presented. Unlike traditional recognition systems which use hand-designed features like SIFT or HOG, our convolutional networks architecture learns new features and representations more appropriate to the classification task in infrared images. Another pedestrian detector based on logistic regression is designed and compared to convolutional networks based classifier. Our system built over non-visible range sensor may have an important role in next generation robotics, especially in perception, advanced driver assistant systems (ADAS) and intelligent surveillance systems.

Original languageEnglish
Pages (from-to)511-522
Number of pages12
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9244
DOIs
Publication statusPublished - 2015
Event8th International Conference on Intelligent Robotics and Applications, ICIRA 2015 - Portsmouth, United Kingdom
Duration: 24 Aug 201527 Aug 2015

Keywords

  • Convolutional neural networks
  • Far infrared imagery
  • Learning features
  • Logistic regression
  • Pedestrian detection

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