An optimized hybrid framework for car theft detection: comparative insights from deep transfer learning and feature-based machine learning

Car theft has become a significant issue in modern societies, with far-reaching individual and social consequences. This criminal act causes substantial financial losses for vehicle owners, undermines public trust in security systems, and increases social and governmental costs. Therefore, research on developing innovative and efficient methods for detecting and preventing car theft holds particular importance. In this study, advanced methods for detecting car theft have been evaluated and compared through two main approaches: deep learning and machine learning. First, pre-trained deep neural networks were examined. In the second phase, various image features were extracted using feature extraction methods, such as Edge Direction Histogram (EDH), Edge Oriented Histogram (EOH), and Histogram Oriented Gradient (HOG), followed by the assessment of machine learning approaches. Finally, a hybrid model based on Hybrid Edge and Gradient-Based Features (HFEM) combined with an XGBoost classifier was proposed, achieving an accuracy of 98.6% in predicting car theft.