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https://www.nexdata.ai/shujutang/static/image/index/datatang_tuxiang_default.webp
[]
837 People Driver Gesture Recognition Dataset – Static & Dynamic Gestures for Automotive AI
driver gesture dataset
automotive AI dataset
in-car gesture recognition
driver monitoring dataset
human computer interaction dataset
vehicle hand gesture dataset
infrared camera dataset
The Driver Gesture Recognition Dataset contains recordings from 837 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 is a paid datasets for commercial use, research purpose and more. Licensed ready made datasets help jump-start AI projects.
![Specifications]()
Specifications
Data size
837 people, each person collects 18 static gestures and 23 dynamic gestures
Gender distribution
417 males, 420 females
Nationality distribution
Vietnam, Indonesia
Age distribution
18~45 years old, 46~60 years old, over 60 years old
Collecting environment
in-car camera shooting scene
Data diversity
covering multiple gestures, multiple age groups, and multiple time periods
Device
visible light and infrared binocular camera, resolution are 1,920x1,080
Shooting position
the center of the inside rearview mirror of the car, above the center console in the car, above the left A-pillar in the car, steering wheel position, rearview mirror wide angle lens position
Collecting time
day, evening, night
Collecting light
normal light, weak light, strong light
Vehicle type
Car, SUV, MVP, Truck, Bus
Data format
the video data format is .mp4, the image data format is .jpg
Accuracy
based on the accuracy of the actions, the accuracy exceeds 95%;the label naming accuracy rate is over 95%
![Sample]()
Sample
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