Computer Vision Datasets

The collecting scenes of this dataset include indoor scenes and outdoor scenes. The data includes male and female. The race distribution includes Asian, Black, Caucasian and Brown people. The age distribution ranges from children to elderly. The collecting device is DV-DH4,044S305AD. The data diversity includes multiple age periods, multiple facial postures, multiple scenes. The data can be used for tasks such as AI-based infrared facial recognition and biometric authentication. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

The 1,444 passenger behavior recognition dataset covers multiple ages, time periods and light exposure. Passenger behavior includes passenger normal behavior, passenger abnormal behavior (passenger motion sickness behavior, passenger sleepiness behavior, passenger lost children & items behaviors). In terms of acquisition equipment, visible and infrared binocular cameras are used. This dataset is suitable for passenger activity recognition, abnormal behavior detection and other tasks.

The data includes multiple age groups, multiple time periods and multiple races (Caucasian, Black, Indian). The passenger behaviors include passenger normal behavior, passenger abnormal behavior (passenger carsick behavior, passenger sleepy behavior, passenger lost items behavior). In terms of device, binocular cameras of RGB and infrared channels were applied. This data can be used for tasks such as in-vehicle passenger monitoring and safety AI systems.

5,993 People – Infrared Face Recognition Data. The collecting scenes of this dataset include indoor scenes and outdoor scenes. The data includes male and female. The age distribution ranges from child to the elderly, the young people and the middle aged are the majorities. The collecting device is realsense D453i. The data diversity includes multiple age periods, multiple facial postures, multiple scenes. The data can be used for tasks such as infrared face recognition.

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 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.