Utilizing Machine Learning and Computer Vision for the Detection of Abusive Behavior in IoT Systems

Abstract

Law enforcement and civilian protection are increasingly dependent on surveillance technologies. To prevent social, economic, and environmental harm, video surveillance situations like those in train stations, schools, and hospitals must automatically detect aggressive and suspicious behavior. Intelligent video surveillance systems now enable human interaction monitoring. Despite its security benefits, crowds and the camera's vision make it difficult to distinguish regular from abnormal activity. Therefore, there is a great deal of investigation into the many methods of identifying violent behavior. The detection approach presented in this research can be broken down into three distinct subfields. The classification methods employed led to the formation of these classes. Classifications include Gradient Descent (SGD), Adaptive boosting (ADA), Naive Base (NB), and Logistic Regression (LR) for detecting violent acts with machine learning. Methods for feature extraction and violence detection are derived using Principal Component Analysis (PCA). Dataset learning-based models for violence detection can also be evaluated using the AIRTLab dataset, a model developed to test the robustness of algorithms against false positives. In order to determine if the proposed model's resistance to false positives, accuracy (ACC) metrics were computed on the proposed model and used to set a benchmark for the AIRTLab dataset. According to the research literature, the proposed models are accurate of 98.31% with the logistic regression classifier having superior generalization skills.