skip to main content
10.1145/3129416.3129437acmotherconferencesArticle/Chapter ViewAbstractPublication PageshtConference Proceedingsconference-collections
research-article

Better feature acquisition through the use of infrared imaging for human detection systems

Published: 26 September 2017 Publication History

Abstract

Human detection on static images remains a challenging research problem. This work evaluates the significance of using infrared imaging (IIR) over several human detection systems. Larger complexities arise when detecting people in colour images due to the possibility of random colour patterns on the image backgrounds and clothes of pedestrians. In most cases, the colour clutter contributes negatively to image representation methods that solely rely on edge information. The basis of our supposition is that the choice of information has a large impact on the robustness of statistical learning systems. To test this supposition, we created and published a new infrared-based pedestrian dataset called "SIGNI" [9].
Several datasets of the same size were prepared and tested on three different classifiers. The classifiers are first trained with popular colour datasets to determine the optimal parameters that obtain high classification rates on unseen samples. Once satisfactory results are obtained, the same parameters are used for training the classifiers with infrared samples. The conventional use of support vector machines (SVM) on HOG features is tested against extreme learning machines (ELM) and convolutional neural networks (CNN). The results obtained show that the reduction of noise clutter improves the quality of acquired HOG features. As slight performance gains were observed during the classification of infrared samples over the use of visual samples.

References

[1]
Bernhard E Boser, Isabelle M Guyon, and Vladimir N Vapnik. 1992. A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory. ACM, 144--152.
[2]
Navneet Dalal and Bill Triggs. 2005. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, Vol. 1. IEEE, 886--893.
[3]
James W Davis and Mark A Keck. 2005. A two-stage template approach to person detection in thermal imagery. In Application of Computer Vision, 2005. WACV/MOTIONS'05 Volume 1. Seventh IEEE Workshops on, Vol. 1. IEEE, 364--369.
[4]
Piotr Dollár, Serge J Belongie, and Pietro Perona. 2010. The Fastest Pedestrian Detector in the West. In Proceedings of the British Machine Vision Conference, Vol. 2. BMVA Press, 7.
[5]
Pedro F Felzenszwalb, Ross B Girshick, David McAllester, and Deva Ramanan. 2010. Object detection with discriminatively trained part-based models. Pattern Analysis and Machine Intelligence, IEEE Transactions on 32, 9 (2010), 1627--1645.
[6]
Gao Huang, Guang-Bin Huang, Shiji Song, and Keyou You. 2015. Trends in extreme learning machines: a review. Neural Networks 61 (2015), 32--48.
[7]
Guang-Bin Huang, Qin-Yu Zhu, and Chee-Kheong Siew. 2006. Extreme learning machine: theory and applications. Neurocomputing 70, 1 (2006), 489--501.
[8]
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105.
[9]
Dumisani Kunene. 2017. SIGNI infrared pedestrian dataset, electronic dataset. Google Drive Storage. (2017). https://goo.gl/ugbevV.
[10]
Yuan Lan, Yeng Chai Soh, and Guang-Bin Huang. 2010. Two-stage extreme learning machine for regression. Neurocomputing 73, 16 (2010), 3028--3038.
[11]
Yann LeCun and Yoshua Bengio. 1995. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks 3361, 10 (1995), 1995.
[12]
Krystian Mikolajczyk, Cordelia Schmid, and Andrew Zisserman. 2004. Human detection based on a probabilistic assembly of robust part detectors. In European Conference on Computer Vision. Springer, 69--82.
[13]
Michael Oren, Constantine Papageorgiou, Pawan Sinha, Edgar Osuna, and Tomaso Poggio. 1997. Pedestrian detection using wavelet templates. In Computer Vision and Pattern Recognition, 1997. Proceedings., IEEE Computer Society Conference on. 193--199.
[14]
Gary Overett, Lars Petersson, Nathan Brewer, Lars Andersson, and Niklas Pettersson. 2008. A new pedestrian dataset for supervised learning. In Intelligent Vehicles Symposium, 2008 IEEE. IEEE, 373--378.
[15]
Jan Portmann, Simon Lynen, Margarita Chli, and Roland Siegwart. 2014. People detection and tracking from aerial thermal views. In Robotics and Automation (ICRA), 2014 IEEE International Conference on. IEEE, 1794--1800.
[16]
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 779--788.
[17]
Tom Ubukata, Masatoshi Shibata, Kotaro Terabayashi, Alessandro Mora, Takehiro Kawashita, Gakuto Masuyama, and Kazunori Umeda. 2014. Fast Human Detection Combining Range Image Segmentation and Local Feature Based Detection. In Pattern Recognition (ICPR), 2014 22nd International Conference on. IEEE, 4281--4286.
[18]
Vladimir Naumovich Vapnik and Vlamimir Vapnik. 1998. Statistical learning theory. Vol. 1. Wiley New York.
[19]
Xiaoyu Wang, Tony X Han, and Shuicheng Yan. 2009. An HOG-LBP human detector with partial occlusion handling. In Computer Vision, 2009 IEEE 12th International Conference on. IEEE, 32--39.
[20]
Yi Yang and Deva Ramanan. 2011. Articulated pose estimation with flexible mixtures-of-parts. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. IEEE, 1385--1392.
[21]
Li Zhang, Bo Wu, and Ram Nevatia. 2007. Pedestrian detection in infrared images based on local shape features. In Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on. IEEE, 1--8.
[22]
Rui Zhang, Yuan Lan, Guang-bin Huang, and Zong-Ben Xu. 2012. Universal approximation of extreme learning machine with adaptive growth of hidden nodes. IEEE Transactions on Neural Networks and Learning Systems 23, 2 (2012), 365--371.
[23]
Liang Zhao and Chuck Thorpe. 2000. Recursive context reasoning for human detection and parts identification. In IEEE Workshop on Human Modeling, Analysis and Synthesis. 136--141.

Cited By

View all
  • (2019)Edge-preserving smoothing filters for improving object classificationProceedings of the South African Institute of Computer Scientists and Information Technologists 201910.1145/3351108.3351125(1-7)Online publication date: 17-Sep-2019
  • (2019)Enhancing edge-based image descriptor models through colour unification2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA)10.1109/RoboMech.2019.8704732(147-152)Online publication date: Jan-2019

Index Terms

  1. Better feature acquisition through the use of infrared imaging for human detection systems

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    SAICSIT '17: Proceedings of the South African Institute of Computer Scientists and Information Technologists
    September 2017
    384 pages
    ISBN:9781450352505
    DOI:10.1145/3129416
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 26 September 2017

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. convolutional neural networks
    2. extreme learning machines
    3. feature extraction
    4. human detection
    5. infrared imaging
    6. noise-reduction
    7. support vector machines

    Qualifiers

    • Research-article

    Funding Sources

    Conference

    SAICSIT '17

    Acceptance Rates

    SAICSIT '17 Paper Acceptance Rate 39 of 108 submissions, 36%;
    Overall Acceptance Rate 187 of 439 submissions, 43%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)3
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 25 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2019)Edge-preserving smoothing filters for improving object classificationProceedings of the South African Institute of Computer Scientists and Information Technologists 201910.1145/3351108.3351125(1-7)Online publication date: 17-Sep-2019
    • (2019)Enhancing edge-based image descriptor models through colour unification2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA)10.1109/RoboMech.2019.8704732(147-152)Online publication date: Jan-2019

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media