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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%. |
