Automatic Selection of Machine Learning Models for Armed People Identification
This research aims to improve the automatic identification of armed people in surveillance videos. We focus on people armed with pistols and revolvers. Furthermore, we use the YOLOv4 to detect people and weapons in each video frame. We developed a series of algorithms to create a dataset with the in...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10763529/ |
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| author | Alonso Javier Amado-Garfias Santiago Enrique Conant-Pablos Jose Carlos Ortiz-Bayliss Hugo Terashima-Marin |
| author_facet | Alonso Javier Amado-Garfias Santiago Enrique Conant-Pablos Jose Carlos Ortiz-Bayliss Hugo Terashima-Marin |
| author_sort | Alonso Javier Amado-Garfias |
| collection | DOAJ |
| description | This research aims to improve the automatic identification of armed people in surveillance videos. We focus on people armed with pistols and revolvers. Furthermore, we use the YOLOv4 to detect people and weapons in each video frame. We developed a series of algorithms to create a dataset with the information extracted from the bounding boxes generated by YOLOv4 in real-time. Thereby, we initially developed six-armed people detectors (APD) based on six machine learning models: Random Forest Classifier (RFC-APD), Multilayer Perceptron (MLP-APD), Support Vector Machine (SVM-APD), Logistic Regression (LR-APD), Naive Bayes (NB-APD), and Gradient Boosting Classifier (GBC-APD). These models use 20 predictors to make their predictions. These predictors are computed from the bounding box coordinates of the detected people and weapons, their distances, and areas of intersection. Based on our results, the RFC-APD was the best-performing detector, with an accuracy of 95.59%, a recall of 94.51%, and an F1-score of 95.65%. In this work, we propose to create selectors for deciding which APD to use in each video frame (APD4F) to improve the detection results. Besides, we implemented two types of APD4Fs, one based on a Random Forest Classifier (RFC-APD4F) and another in a Multilayer Perceptron (MLP-APD4F). We developed 44 APD4Fs combining subsets of the six APDs. Both APD4F types outperformed most of the independent use of all six APDs. A multilayer perceptron-based APD4F, which combines an MLP-APD, a NB-APD, and a LR-APD, presented the best performance, achieving an accuracy of 95.84%, a recall of 99.28% and an F1 score of 96.07%. |
| format | Article |
| id | doaj-art-2311ccf26cd648b89f510d1804c78b27 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2311ccf26cd648b89f510d1804c78b272024-12-04T00:01:43ZengIEEEIEEE Access2169-35362024-01-011217595217596810.1109/ACCESS.2024.350448310763529Automatic Selection of Machine Learning Models for Armed People IdentificationAlonso Javier Amado-Garfias0https://orcid.org/0000-0001-8447-3355Santiago Enrique Conant-Pablos1https://orcid.org/0000-0001-6270-3164Jose Carlos Ortiz-Bayliss2https://orcid.org/0000-0003-3408-2166Hugo Terashima-Marin3https://orcid.org/0000-0002-5320-0773School of Engineering and Sciences, Tecnológico de Monterrey, Monterrey, Nuevo Leon, MexicoSchool of Engineering and Sciences, Tecnológico de Monterrey, Monterrey, Nuevo Leon, MexicoSchool of Engineering and Sciences, Tecnológico de Monterrey, Monterrey, Nuevo Leon, MexicoSchool of Engineering and Sciences, Tecnológico de Monterrey, Monterrey, Nuevo Leon, MexicoThis research aims to improve the automatic identification of armed people in surveillance videos. We focus on people armed with pistols and revolvers. Furthermore, we use the YOLOv4 to detect people and weapons in each video frame. We developed a series of algorithms to create a dataset with the information extracted from the bounding boxes generated by YOLOv4 in real-time. Thereby, we initially developed six-armed people detectors (APD) based on six machine learning models: Random Forest Classifier (RFC-APD), Multilayer Perceptron (MLP-APD), Support Vector Machine (SVM-APD), Logistic Regression (LR-APD), Naive Bayes (NB-APD), and Gradient Boosting Classifier (GBC-APD). These models use 20 predictors to make their predictions. These predictors are computed from the bounding box coordinates of the detected people and weapons, their distances, and areas of intersection. Based on our results, the RFC-APD was the best-performing detector, with an accuracy of 95.59%, a recall of 94.51%, and an F1-score of 95.65%. In this work, we propose to create selectors for deciding which APD to use in each video frame (APD4F) to improve the detection results. Besides, we implemented two types of APD4Fs, one based on a Random Forest Classifier (RFC-APD4F) and another in a Multilayer Perceptron (MLP-APD4F). We developed 44 APD4Fs combining subsets of the six APDs. Both APD4F types outperformed most of the independent use of all six APDs. A multilayer perceptron-based APD4F, which combines an MLP-APD, a NB-APD, and a LR-APD, presented the best performance, achieving an accuracy of 95.84%, a recall of 99.28% and an F1 score of 96.07%.https://ieeexplore.ieee.org/document/10763529/Machine learningarmed people detectioncomputer visionobject detectionYOLO |
| spellingShingle | Alonso Javier Amado-Garfias Santiago Enrique Conant-Pablos Jose Carlos Ortiz-Bayliss Hugo Terashima-Marin Automatic Selection of Machine Learning Models for Armed People Identification IEEE Access Machine learning armed people detection computer vision object detection YOLO |
| title | Automatic Selection of Machine Learning Models for Armed People Identification |
| title_full | Automatic Selection of Machine Learning Models for Armed People Identification |
| title_fullStr | Automatic Selection of Machine Learning Models for Armed People Identification |
| title_full_unstemmed | Automatic Selection of Machine Learning Models for Armed People Identification |
| title_short | Automatic Selection of Machine Learning Models for Armed People Identification |
| title_sort | automatic selection of machine learning models for armed people identification |
| topic | Machine learning armed people detection computer vision object detection YOLO |
| url | https://ieeexplore.ieee.org/document/10763529/ |
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