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Home > All Category Datasets > Computer Vision Datasets > 558,870 Videos – Dynamic Gesture Recognition Dataset with 50 Gesture Types

558,870 Videos – Dynamic Gesture Recognition Dataset with 50 Gesture Types

Dynamic gesture video dataset

Gesture recognition videos dataset

Human gesture video dataset

Multi-angle gesture video dataset

This dataset contains 558,870 videos of 50 types of dynamic gestures. The collecting scenes of this dataset include indoor scenes and outdoor scenes (natural scenery, street view, square, etc.). The data covers both males and females, with an age range from teenagers to seniors. The data diversity includes multiple scenes, 50 types of dynamic gestures, 5 photographic angles, multiple light conditions, different photographic distances. This data can be used for dynamic gesture recognition in smart homes, audio equipment, and on-board 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

558,870 videos, 219,660 videos were collected by laptop, 339,210 videos were collected by cellphone or iPad

Population distribution

Asian, the gender distribution is male and female, the age distribution is under 18 years old, 18-40 years old, and over 40 years old

Collection environment

including indoor scenes and outdoor scenes (natural scenery, street view, square, etc.)

Data diversity

including multiple scenes, 50 types of dynamic gestures, 5 photographic angles, multiple light conditions, different photographic distances

Device

cellphone, iPad, laptop

Collecting angle

front, left, right, up and down

Collecting distance

0.3m, 0.6m, 1m, 2m, 3m

Data format

the video data format is .mp4, .mov, .wmv

Collecting content

dynamic gestures of the left and right hand were collected at 5 photographic angles respectively

Accuracy

based on the accuracy of the gesture actions, the accuracy exceeds 97%; the accuracy of the video naming exceeds 97%; the accuracy of labels annotation is not less than 97%

Sample

Sample

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