EgoGesture: A New Dataset and Benchmark for Egocentric Hand Gesture Recognition

By Yifan Zhang, Congqi Cao, Jian Cheng, Hanqing Lu. IEEE transactions on multimedia, 2018

319 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Skeleton-Based Action Recognition With Directed Graph Neural Networks (2019, background): “Action recognition, which plays an essential role in video surveillance and human-computer interaction, has been widely investigated but not yet fully addressed [27, 31, 36, 5, 32, 38].”
MMTM: Multimodal Transfer Module for CNN Fusion (2019, methodology): “We use a batch size of 4 containing 64 -frames ( 32 -frames for EgoGesture ) snip-pets in the training stage.”
Hands Holding Clues for Object Recognition in Teachable Machines (2019, background): “, [13, 30, 72]) with many applications in virtual reality (e.”
Predicting the Future from First Person (Egocentric) Vision: A Survey (2021, unknown): “EgoGesture [96]: A dataset designed for gesture recognition, consisting of 24.161 videos of 50 subjects performing 83 di ﬀ erent gestures.”
Skeleton-Based Action Recognition With Multi-Stream Adaptive Graph Convolutional Networks (2019, unknown): “[7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22].”
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition (2018, background): “Action recognition methods based on skeleton data have been widely investigated and attracted considerable attention due to their strong adaptability to the dynamic circumstance and complicated background [31, 8, 6, 27, 22, 29, 33, 19, 20, 21, 14, 13, 23, 18, 17, 32, 30, 34].”