research-article

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

Authors:

Dumisani Kunene,

Hima VadapalliAuthors Info & Claims

SAICSIT '17: Proceedings of the South African Institute of Computer Scientists and Information Technologists

Article No.: 21, Pages 1 - 10

https://doi.org/10.1145/3129416.3129437

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.

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Cited By

Skosana VKunene Dde Villiers CSmuts H(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
https://dl.acm.org/doi/10.1145/3351108.3351125
Kunene DSkosana V(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
https://doi.org/10.1109/RoboMech.2019.8704732

Index Terms

Better feature acquisition through the use of infrared imaging for human detection systems
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition

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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

Conference Chair:
Muthoni Masinde
Central University of Technology

Copyright © 2017 ACM.

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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 September 2017

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University of the Witwatersrand, Johannesburg
University of the Witswatersrand, Johannesburg
The Council for Scientific and Industrial Research
Council for Scientific and Industrial Research

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SAICSIT '17

SAICSIT '17: South African Institute of Computer Scientists and Information Technologists

September 26 - 28, 2017

Thaba 'Nchu, South Africa

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SAICSIT '17 Paper Acceptance Rate 39 of 108 submissions, 36%;

Overall Acceptance Rate 187 of 439 submissions, 43%

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Cited By

Skosana VKunene Dde Villiers CSmuts H(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
https://dl.acm.org/doi/10.1145/3351108.3351125
Kunene DSkosana V(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
https://doi.org/10.1109/RoboMech.2019.8704732

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