A Pedestrian Detection Method Based on Genetic Algorithm for Optimize XGBoost Training Parameters
In this paper, we present a machine learning classifier which is used for pedestrian detection based on XGBoost. Our approach, the Genetic Algorithm is introduced to optimize the parameter tuning process during training an XGBoost model. In order to improve the classification accuracy, HOG and LBP f...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8807116/ |
| _version_ | 1818667666148163584 |
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| author | Yu Jiang Guoxiang Tong Henan Yin Naixue Xiong |
| author_facet | Yu Jiang Guoxiang Tong Henan Yin Naixue Xiong |
| author_sort | Yu Jiang |
| collection | DOAJ |
| description | In this paper, we present a machine learning classifier which is used for pedestrian detection based on XGBoost. Our approach, the Genetic Algorithm is introduced to optimize the parameter tuning process during training an XGBoost model. In order to improve the classification accuracy, HOG and LBP features are used to describe pedestrians in a way of tandem fusion, then input into GA-XGBoost classifier proposed in this paper to form a new static image pedestrian detection algorithm. The pedestrian feature extraction and machine learning are decoupled by storing the extracted pedestrian feature as feature files in the experiment, so that training can be exacuted many times and algorithms can be camparied conveniently. Experimental show that our pedestrian detection algorithm has improved the accuracy of pedestrian detection in the static image. The Area Under the ROC Curve (AUC) value reaches 0.9913. |
| first_indexed | 2024-12-17T06:24:03Z |
| format | Article |
| id | doaj.art-7cdbe2121f6a43d7a81a4b398bb371ec |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T06:24:03Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7cdbe2121f6a43d7a81a4b398bb371ec2022-12-21T22:00:20ZengIEEEIEEE Access2169-35362019-01-01711831011832110.1109/ACCESS.2019.29364548807116A Pedestrian Detection Method Based on Genetic Algorithm for Optimize XGBoost Training ParametersYu Jiang0https://orcid.org/0000-0002-1184-8037Guoxiang Tong1Henan Yin2Naixue Xiong3https://orcid.org/0000-0002-0394-4635Shanghai Key Laboratory of Modern Optical System, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaShanghai Key Laboratory of Modern Optical System, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaShanghai Key Laboratory of Modern Optical System, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaCollege of Intelligence and Computing, Tianjin University, Tianjin, ChinaIn this paper, we present a machine learning classifier which is used for pedestrian detection based on XGBoost. Our approach, the Genetic Algorithm is introduced to optimize the parameter tuning process during training an XGBoost model. In order to improve the classification accuracy, HOG and LBP features are used to describe pedestrians in a way of tandem fusion, then input into GA-XGBoost classifier proposed in this paper to form a new static image pedestrian detection algorithm. The pedestrian feature extraction and machine learning are decoupled by storing the extracted pedestrian feature as feature files in the experiment, so that training can be exacuted many times and algorithms can be camparied conveniently. Experimental show that our pedestrian detection algorithm has improved the accuracy of pedestrian detection in the static image. The Area Under the ROC Curve (AUC) value reaches 0.9913.https://ieeexplore.ieee.org/document/8807116/Pedestrain detectionhistogram of oriented gradient features (HOG)local binary patterns (LBP) XGBoost classifiergenetic algorithm |
| spellingShingle | Yu Jiang Guoxiang Tong Henan Yin Naixue Xiong A Pedestrian Detection Method Based on Genetic Algorithm for Optimize XGBoost Training Parameters IEEE Access Pedestrain detection histogram of oriented gradient features (HOG) local binary patterns (LBP) XGBoost classifier genetic algorithm |
| title | A Pedestrian Detection Method Based on Genetic Algorithm for Optimize XGBoost Training Parameters |
| title_full | A Pedestrian Detection Method Based on Genetic Algorithm for Optimize XGBoost Training Parameters |
| title_fullStr | A Pedestrian Detection Method Based on Genetic Algorithm for Optimize XGBoost Training Parameters |
| title_full_unstemmed | A Pedestrian Detection Method Based on Genetic Algorithm for Optimize XGBoost Training Parameters |
| title_short | A Pedestrian Detection Method Based on Genetic Algorithm for Optimize XGBoost Training Parameters |
| title_sort | pedestrian detection method based on genetic algorithm for optimize xgboost training parameters |
| topic | Pedestrain detection histogram of oriented gradient features (HOG) local binary patterns (LBP) XGBoost classifier genetic algorithm |
| url | https://ieeexplore.ieee.org/document/8807116/ |
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