Computer Vision Datasets

The data includes multiple ages, time periods and races (Caucasian, Black, Indian). The driver behaviors includes dangerous behavior, fatigue behavior and visual movement behavior. In terms of device, binocular cameras of RGB and infrared channels were applied. This dataset is ideal for driver behavior analysis, fatigue detection, attention monitoring, and developing intelligent in-cabin monitoring systems for automotive safety applications.

This dataset includes multiple ages and multiple time periods. The driver behaviors includes dangerous behavior, fatigue behavior and visual movement behavior. In terms of device, binocular cameras of RGB and infrared channels were applied. This dataset is suitable for driver behavior analysis, fatigue and attention monitoring, dangerous driving detection, and training AI models for intelligent in-cabin monitoring systems.

This dataset includes multiple ages, multiple time periods and behaviors (dangerous behaviors, fatigue behaviors, visual movement behaviors). In terms of annotation, 72 facial landmarks (including pupils), face attributes, gesture bounding boxes, seatbelt bounding boxes, pupil landmarks and behavior categories were annotated in the data. This data can be used for tasks such as driver behavior analysis, fatigue detection, attention monitoring, and training AI models for intelligent in-cabin systems.

This dataset includes diverse drivers across multiple ages, time periods, and lighting conditions, with annotated behaviors including dangerous driving, fatigue, and visual movement behaviors. In terms of device, binocular cameras of RGB and infrared channels were applied. This data can be used for tasks such as driver behavior analysis, fatigue and drowsiness detection, dangerous driving recognition and driver attention and visual movement monitoring.

This dataset contains facial expression recognition data from 1,323 drivers, recorded under different ages, time periods, and expression variations. In terms of acquisition equipment, RGB and infrared binocular cameras are used. This dataset of driver expression recognition data can be used for driver monitoring systems (DMS), driver fatigue and drowsiness detection, In-cabin behavior analysis and other tasks.

The Driver Gesture Recognition Dataset contains recordings from 1,334 participants across diverse age groups, time periods and multiple gestures. Each person performed 18 static gestures(such as fist-clenching gestures and heart-to-heart gestures) and 23 dynamic gestures(including index finger clicks and two-finger clicks). In terms of acquisition equipment, visible light and infrared binocular cameras are used. This dataset is ideal for tasks such as driver monitoring, gesture recognition in vehicles, human-computer interaction, and driver assistance systems.

This dataset includes 9,000 images from 180 individuals, each labeled with 21 hand gesture landmarks along with attributes such as visibility, gesture type, gender, age, nationality, and vehicle type. Captured across diverse driving environments and time periods, this data supports research and model training in gesture recognition, pose estimation, and driver behavior analysis. It is especially useful for developing driver monitoring systems (DMS) and in-cabin interaction technologies. All data is compliant with GDPR, CCPA, and PIPL regulations and has been quality verified by leading AI companies.

This dataset contains 28,972 high-resolution infrared images of 100 individuals in driver scenarios, each annotated with face bounding boxes and 96 facial landmarks. The participants vary in age and race (Caucasian, Black, Indian), and the data reflects realistic driving behaviors including fatigue, distraction, and visual movement. Each subject contributes between 274 and 299 images. Infrared cameras were used to ensure visibility under varying lighting conditions. This dataset is suitable for facial detection, keypoint tracking, driver monitoring system (DMS) development, and other computer vision applications. All data complies with GDPR, CCPA, and PIPL regulations.

This 3D high-fidelity synthetic dataset simulates real-world Driver Monitoring System (DMS) environments using photorealistic 3D scene modeling. It includes multi-modal sensor outputs such as camera images, videos, and point clouds, all generated through simulation. The dataset is richly annotated with object classification, detection, and segmentation labels, as well as human pose data (head, eye, arm, and leg position/orientation), camera parameters, and temporal metadata such as illumination and weather conditions. Ideal for training and evaluating models in autonomous driving, robotics, driver monitoring, computer vision, and synthetic perception tasks.