Pedestrian classification and detection in far infrared images

Atmane Khellal; Hongbin Ma; Qing Fei

doi:10.1007/978-3-319-22879-2_47

Pedestrian classification and detection in far infrared images

Atmane Khellal, Hongbin Ma^*, Qing Fei

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

Research output: Contribution to journal › Conference article › peer-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 language	English
Pages (from-to)	511-522
Number of pages	12
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9244
DOIs	https://doi.org/10.1007/978-3-319-22879-2_47
Publication status	Published - 2015
Event	8th International Conference on Intelligent Robotics and Applications, ICIRA 2015 - Portsmouth, United Kingdom Duration: 24 Aug 2015 → 27 Aug 2015

Keywords

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

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10.1007/978-3-319-22879-2_47

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title = "Pedestrian classification and detection in far infrared images",

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.",

keywords = "Convolutional neural networks, Far infrared imagery, Learning features, Logistic regression, Pedestrian detection",

author = "Atmane Khellal and Hongbin Ma and Qing Fei",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 8th International Conference on Intelligent Robotics and Applications, ICIRA 2015 ; Conference date: 24-08-2015 Through 27-08-2015",

year = "2015",

doi = "10.1007/978-3-319-22879-2_47",

language = "English",

volume = "9244",

pages = "511--522",

journal = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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TY - JOUR

T1 - Pedestrian classification and detection in far infrared images

AU - Khellal, Atmane

AU - Ma, Hongbin

AU - Fei, Qing

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - Convolutional neural networks

KW - Far infrared imagery

KW - Learning features

KW - Logistic regression

KW - Pedestrian detection

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U2 - 10.1007/978-3-319-22879-2_47

DO - 10.1007/978-3-319-22879-2_47

M3 - Conference article

AN - SCOPUS:84985003707

SN - 0302-9743

VL - 9244

SP - 511

EP - 522

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

T2 - 8th International Conference on Intelligent Robotics and Applications, ICIRA 2015

Y2 - 24 August 2015 through 27 August 2015

ER -

Pedestrian classification and detection in far infrared images

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Keywords

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