research-article

Fast pedestrian detection using multimodal estimation of distribution algorithms

Authors:

Wei-Jie YuAuthors Info & Claims

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference

Pages 1248 - 1255

https://doi.org/10.1145/3071178.3071237

Published: 01 July 2017 Publication History

Abstract

Pedestrian detection plays a pivotal role in various domains but is still a challenging problem nowadays. In this study, we transform the multiple-pedestrian detection problem into a multimodal optimization problem and then utilize a multimodal estimation of distribution algorithm (MEDA) to optimize this problem based on Histograms of Oriented Gradients (HOG) feature and Support Vector Machines (SVM). Specifically, we adopt a three-dimensional vector to represent a rectangular region of an image and also use it to encode individuals. Then, a state-of-the-art multimodal optimization algorithm called MEDA is utilized to evolve the individuals, so that a series of optimal rectangular regions containing pedestrians can be obtained. Experiments conducted on a set of images from one pedestrian dataset called INRIA confirm that in comparison with the classical HOG-SVM method and one state-of-the-art method, the developed algorithm cannot only achieve higher detection accuracy on images containing different numbers of pedestrians, but also can remain high computational efficiency.

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Cited By

Huang YWang ZZhang YWang YKwong SZhang J(2024)Wireless sensor networks-based adaptive differential evolution for multimodal optimization problemsApplied Soft Computing10.1016/j.asoc.2024.111541158(111541)Online publication date: Jun-2024
https://doi.org/10.1016/j.asoc.2024.111541
Wang ZZhan ZLi YKwong SJeon SZhang J(2023)Fitness and Distance Based Local Search With Adaptive Differential Evolution for Multimodal Optimization ProblemsIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2023.32345757:3(684-699)Online publication date: Jun-2023
https://doi.org/10.1109/TETCI.2023.3234575
Jiang YZhan ZTan KZhang J(2023)Optimizing Niche Center for Multimodal Optimization ProblemsIEEE Transactions on Cybernetics10.1109/TCYB.2021.312536253:4(2544-2557)Online publication date: Apr-2023
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Index Terms

Fast pedestrian detection using multimodal estimation of distribution algorithms
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
    2. Search methodologies

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cover image ACM Conferences

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference

July 2017

1427 pages

ISBN:9781450349208

DOI:10.1145/3071178

General Chair:
Peter A. N. Bosman
Centrum Wiskunde & Informatica (CWI)

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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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

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Publication History

Published: 01 July 2017

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Funding Sources

Natural Science Foundation of Guangdong
Guangzhou Pearl River New Star of Science and Technology Project
Guangdong Special Support Program
National Natural Science Foundation of China

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GECCO '17

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SIGEVO

GECCO '17: Genetic and Evolutionary Computation Conference

July 15 - 19, 2017

Berlin, Germany

Acceptance Rates

GECCO '17 Paper Acceptance Rate 178 of 462 submissions, 39%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Huang YWang ZZhang YWang YKwong SZhang J(2024)Wireless sensor networks-based adaptive differential evolution for multimodal optimization problemsApplied Soft Computing10.1016/j.asoc.2024.111541158(111541)Online publication date: Jun-2024
https://doi.org/10.1016/j.asoc.2024.111541
Wang ZZhan ZLi YKwong SJeon SZhang J(2023)Fitness and Distance Based Local Search With Adaptive Differential Evolution for Multimodal Optimization ProblemsIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2023.32345757:3(684-699)Online publication date: Jun-2023
https://doi.org/10.1109/TETCI.2023.3234575
Jiang YZhan ZTan KZhang J(2023)Optimizing Niche Center for Multimodal Optimization ProblemsIEEE Transactions on Cybernetics10.1109/TCYB.2021.312536253:4(2544-2557)Online publication date: Apr-2023
https://doi.org/10.1109/TCYB.2021.3125362
Chen XZhao HLiu J(2023)A network community-based differential evolution for multimodal optimization problemsInformation Sciences: an International Journal10.1016/j.ins.2023.119359645:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.ins.2023.119359
Wu XLin QLin WYe YZhu QLeung V(2023)A Kriging model-based evolutionary algorithm with support vector machine for dynamic multimodal optimizationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106039122(106039)Online publication date: Jun-2023
https://doi.org/10.1016/j.engappai.2023.106039
Wang RHao KHuang BZhu X(2023)Adaptive niching particle swarm optimization with local search for multimodal optimizationApplied Soft Computing10.1016/j.asoc.2022.109923133:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.asoc.2022.109923
Orujpour MFeizi-Derakhshi MAkan T(2023)A multimodal butterfly optimization using fitness-distance balanceSoft Computing10.1007/s00500-023-09074-z27:23(17909-17922)Online publication date: 3-Sep-2023
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Wang ZZhou YZhang J(2022)Adaptive Estimation Distribution Distributed Differential Evolution for Multimodal Optimization ProblemsIEEE Transactions on Cybernetics10.1109/TCYB.2020.303869452:7(6059-6070)Online publication date: Jul-2022
https://doi.org/10.1109/TCYB.2020.3038694
Jiang YChen CZhan ZLi YZhang J(2022)Adversarial Differential Evolution for Multimodal Optimization Problems2022 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC55065.2022.9870298(1-8)Online publication date: 18-Jul-2022
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