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

^*此作品的通讯作者

自动化学院

Beijing Institute of Technology

科研成果: 期刊稿件 › 会议文章 › 同行评审

19 引用（Scopus）

摘要

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.

源语言	英语
页（从-至）	511-522
页数	12
期刊	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
卷	9244
DOI	https://doi.org/10.1007/978-3-319-22879-2_47
出版状态	已出版 - 2015
活动	8th International Conference on Intelligent Robotics and Applications, ICIRA 2015 - Portsmouth, 英国期限: 24 8月 2015 → 27 8月 2015

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

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Khellal, A., Ma, H., & Fei, Q. (2015). Pedestrian classification and detection in far infrared images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9244, 511-522. https://doi.org/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",

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

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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

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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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