Improving Armed People Detection on Video Surveillance Through Heuristics and Machine Learning Models

Abstract

Much research aims to enhance weapon detection by applying different techniques to object detection models. However, little research focuses on identifying armed people through real-time surveillance cameras. The proposed solution involves the development of algorithms for identifying people carrying handguns (pistols and revolvers). We have chosen the YOLOv4 model to detect people, guns, and faces. Then, we extract information from YOLO related to real-time videos, such as bounding box coordinates, distances, and intersection areas between firearms and the people in each video frame to recognize the armed people. There are some challenges to overcome, for example, occlusion, hidden handguns, and people close to each other. It allows us to develop and compare different types of solutions. We proposed three heuristics and seven machine-learning models. The heuristics are the method of centers, the method of intersections, and the method of distances. Furthermore, the machine learning models are Random Forest Classifier, Multilayer Perceptron, k-Nearest-Neighbors, Support Vector Machine, Logistic Regression, Naive Bayes, and Gradient Boosting Classifier. The Random Forest Classifier presented the best performance reaching an accuracy of 85.44%, a precision of 87.07%, a recall of 88.68%, and an F1-score of 87.87%.

Keywords

• Armed people detection
• machine learning
• heuristics
• computer vision