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Pedestrian detection for intelligent transportation systems combining AdaBoost algorithm and support vector machine

Authors:

Ming-Heng Zhang,

Yi-Bing ZhaoAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 39, Issue 4

Pages 4274 - 4286

https://doi.org/10.1016/j.eswa.2011.09.106

Published: 01 March 2012 Publication History

Abstract

Pedestrians are the vulnerable participants in transportation system when crashes happen. It is important to detect pedestrian efficiently and accurately in many computer vision applications, such as intelligent transportation systems (ITSs) and safety driving assistant systems (SDASs). This paper proposes a two-stage pedestrian detection method based on machine vision. In the first stage, AdaBoost algorithm and cascading method are adopted to segment pedestrian candidates from image. To confirm whether each candidate is pedestrian or not, a second stage is needed to eliminate some false positives. In this stage, a pedestrian recognizing classifier is trained with support vector machine (SVM). The input features used for SVM training are extracted from both the sample gray images and edge images. Finally, the performance of the proposed pedestrian detection method is tested with real-world data. Results show that the performance is better than conventional single-stage classifier, such as AdaBoost based or SVM based classifier.

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Hao SLiu ZMa XWu YHe TLi J(2024)RCSLFNet: a novel real-time pedestrian detection network based on re-parameterized convolution and channel-spatial location fusion attention for low-resolution infrared imageJournal of Real-Time Image Processing10.1007/s11554-024-01469-x21:3Online publication date: 11-May-2024
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Pedestrian detection for intelligent transportation systems combining AdaBoost algorithm and support vector machine
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
  2. Machine learning

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cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 39, Issue 4

March, 2012

736 pages

ISSN:0957-4174

Issue’s Table of Contents

Copyright © Elsevier Ltd © 2011.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 March 2012

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Cited By

Bahashwan AIbrahim ROmar MAmosa T(2024)Supervised learning-based multi-site lean blowout prediction for dry low emission gas turbineExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.123035244:COnline publication date: 15-Jun-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.123035
Omar MKumar P(2024)PD-ITS: Pothole Detection Using YOLO Variants for Intelligent Transport SystemSN Computer Science10.1007/s42979-024-02887-15:5Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1007/s42979-024-02887-1
Hao SLiu ZMa XWu YHe TLi J(2024)RCSLFNet: a novel real-time pedestrian detection network based on re-parameterized convolution and channel-spatial location fusion attention for low-resolution infrared imageJournal of Real-Time Image Processing10.1007/s11554-024-01469-x21:3Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1007/s11554-024-01469-x
Bulut AOzdemir FBostanci YSoyturk M(2023)Performance Evaluation of Recent Object Detection Models for Traffic Safety Applications on EdgeProceedings of the 2023 5th International Conference on Image Processing and Machine Vision10.1145/3582177.3582178(1-6)Online publication date: 13-Jan-2023
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Sangare MGupta SBouzefrane SBanerjee SMuhlethaler P(2021)Exploring the forecasting approach for road accidentsExpert Systems with Applications: An International Journal10.1016/j.eswa.2020.113855167:COnline publication date: 1-Apr-2021
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Asteris PKoopialipoor MArmaghani DKotsonis ELourenço P(2021)Prediction of cement-based mortars compressive strength using machine learning techniquesNeural Computing and Applications10.1007/s00521-021-06004-833:19(13089-13121)Online publication date: 1-Oct-2021
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Thu MSuvonvorn N(2020)Pyramidal Part-Based Model for Partial Occlusion Handling in Pedestrian ClassificationAdvances in Multimedia10.1155/2020/61535802020Online publication date: 24-Feb-2020
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Artal-Villa LHussein AOlaverri-Monreal C(2019)Extension of the 3DCoAutoSim to Simulate Vehicle and Pedestrian Interaction based on SUMO and Unity 3D2019 IEEE Intelligent Vehicles Symposium (IV)10.1109/IVS.2019.8814253(885-890)Online publication date: 9-Jun-2019
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