The EgoGesture dataset contains 2,081 RGB-D videos, 24,161 gesture samples and 2,953,224 frames from 50 distinct subjects.
30 PAPERS • 2 BENCHMARKS
The NVGesture dataset focuses on touchless driver controlling. It contains 1532 dynamic gestures fallen into 25 classes. It includes 1050 samples for training and 482 for testing. The videos are recorded with three modalities (RGB, depth, and infrared).
23 PAPERS • 1 BENCHMARK
Jester Gesture Recognition dataset includes 148,092 labeled video clips of humans performing basic, pre-defined hand gestures in front of a laptop camera or webcam. It is designed for training machine learning models to recognize human hand gestures like sliding two fingers down, swiping left or right and drumming fingers.
16 PAPERS • 6 BENCHMARKS
The OREBA dataset aims to provide a comprehensive multi-sensor recording of communal intake occasions for researchers interested in automatic detection of intake gestures. Two scenarios are included, with 100 participants for a discrete dish and 102 participants for a shared dish, totalling 9069 intake gestures. Available sensor data consists of synchronized frontal video and IMU with accelerometer and gyroscope for both hands.
3 PAPERS • NO BENCHMARKS YET
Driver Micro Hand Gestures (DriverMHG) is a dataset for dynamic recognition of driver micro hand gestures, which consists of RGB, depth and infrared modalities.
1 PAPER • NO BENCHMARKS YET
MlGesture is a dataset for hand gesture recognition tasks, recorded in a car with 5 different sensor types at two different viewpoints. The dataset contains over 1300 hand gesture videos from 24 participants and features 9 different hand gesture symbols. One sensor cluster with five different cameras is mounted in front of the driver in the center of the dashboard. A second sensor cluster is mounted on the ceiling looking straight down.