https://www.kaggle.com/c/odhgdata/overview
Recent virtual or augmented reality devices offer new 3D environment in which we need new and precise manner to interact. Hands can offer an intuitive and effective tool in these applications. Some gestures, as swipes, are more defined by the hand motion (called here coarse gesture) while others are defined by the hand shape through the gesture (called fine gesture). This difference between useful gestures have to be taken into account in a hand gesture recognition algorithm.
Effective and inexpensive depth sensors, like the Intel RealSense or the Leap Motion Controller provide precise skeletal data of the hand and fingers in the form of a full 3D skeleton corresponding to 22 joints. Hand skeletal data could handle a precise information of the hand shape that HCI applications need in order to use the hand as a manipulation tool. In this challenge, we present a 3D dynamic hand gesture dataset which provides sequences of hand gestures in the form of 3D skeletal data.
The Intel RealSense short range depth camera is used to collect our dataset.
Quentin De Smedt, Hazem Wannous, Jean-Philippe Vandeborre: Heterogeneous hand gesture recognition using 3D dynamic skeletal data. Computer Vision and Image Understanding 181: 60-72 (2019)
Quentin De Smedt, Hazem Wannous, Jean-Philippe Vandeborre, Joris Guerry, Bertrand Le Saux, David Filliat: 3D Hand Gesture Recognition Using a Depth and Skeletal Dataset. 3DOR 2017
Dennis Ludl, Thomas Gulde, and Crist'obal Curio, Simple yet efficient real-time pose-based action recognition. In IEEE Intelligent Computer Science ITSC 2019
Chao Li ; Qiaoyong Zhong ; Di Xie, Shiliang Pu, Skeleton-based action recognition with convolutional neural networks, IEEE International Conference on Multimedia & Expo Workshops (ICMEW 2017)
Chuankun Li ; Yonghong Hou ; Pichao Wang ; Wanqing Li, Joint Distance Maps Based Action Recognition with Convolutional Neural Networks, IEEE Signal Processing Letters, Vol: 24 , Issue: 5, May 2017
Chuankun Li ; Pichao Wang ; Shuang Wang ; Yonghong Hou ; Wanqing Li, Skeleton-based action recognition using LSTM and CNN, IEEE International Conference on Multimedia & Expo Workshops (ICMEW 2017)