HOG Ensembled Boosting Machine Learning Approach for Violent Video Classification

Background: With the proliferation of machine learning and its applications in a variety spheres that are important to humans their day-to-day lives, there is pressing need for automatic detection models can identify abnormal behaviors or acts violence. Methods: This study examines model uses ensemble boosting histograms oriented gradients (HOG) detect violent content from feature vector with single parameter. Findings: The tests performed on two benchmark datasets, such as Hockey Dataset Peliculas dataset, reveal high level performance accuracy classification videos. experiment findings show suggested violence performs well terms average metrics, accuracy, precision, recall being 90.50%, 91.80%, 89.70%, respectively. Novelty applications: proposed method capable striking balance between limited number parameters, result, it be implemented minimal investment computational resources. Keywords: Violence detection; Computer Vision; Action Recognition; Machine Learning; Histogram Oriented Gradients (HOG); Ensemble Boosting