Behavior Analysis of Drivers Using Machine Learning

Driver drowsiness and distraction are major contributors to road accidents worldwide. To address these concerns, this research paper presents a novel approach to implementing a safe driving system that incorporates four main components: eye detection, yawning detection, hand movement detection, and driver's head pose detection. The proposed system utilizes image processing techniques for accurate and real-time monitoring of these parameters. And by providing timely alerts and interventions, the proposed system has the potential to enhance road safety and reduce the occurrence of accidents caused by these factors. Eye detection algorithms are employed to analyze the driver's eye movements and determine the level of drowsiness based on factors such as eye closure and blinking frequency. Yawning detection algorithms focus on identifying specific facial movements associated with fatigue, providing an additional indicator of drowsiness. Hand movement detection algorithms are integrated to monitor driver actions, detecting sudden or prolonged periods of inactivity that may indicate distraction. Additionally, driver's head pose detection algorithms analyze head positions and movements to identify abnormal behaviors that might be indicative of drowsiness or distraction. To validate the effectiveness of the proposed system, extensive experiments are conducted using a diverse dataset of drivers in various driving scenarios. The results demonstrate the system's ability to accurately detect and classify instances of drowsiness and distraction, with high precision and recall rates.