Driver Simulated Driving Control Posture Image Dataset

In the field of traffic driving, as intelligent vehicles and assisted driving technologies continuously develop, driver posture recognition has become a key factor in improving driving safety and experience. However, existing posture recognition technologies often face challenges such as complex and variable environments, data annotation difficulties, and insufficient model generalization abilities. Traditional data sources lack diversity and annotation accuracy, resulting in poor algorithm performance in practical applications. This dataset focuses on addressing the challenges in image data collection and annotation in posture recognition, aiming to provide high-quality driver control posture image data to assist researchers in training and validating algorithms in real scenarios. Data collection uses high-definition camera equipment, simulating driving environments to capture driver postures from multiple angles such as front and side views. During collection, diversity in different lighting and weather conditions is ensured. Quality control measures include multiple rounds of annotation, consistency checks, and expert reviews to ensure the data's accuracy and consistency. The annotation team comprises experts in the fields of traffic engineering and computer vision with extensive industry experience and technical background. The data undergoes rigorous preprocessing steps, using image enhancement and noise reduction techniques, making it suitable for various model training. Data is organized in JPG format, stored according to time sequence and scene classification. The dataset has significant advantages in annotation accuracy, consistency, and completeness, with annotation accuracy exceeding 95%, covering various driving scenarios, and providing comprehensive posture analysis data. Innovations include the introduction of new annotation technologies and data augmentation methods, enhancing the reliability of model training. In practical applications, this dataset significantly improves the posture recognition accuracy of deep learning models, increases the real-time capabilities of systems, better encompasses a wide range of driving scenarios compared to other datasets, and is more evenly annotated. Additionally, the dataset is highly scalable and can easily be used for other traffic-related research.