This repository contains annotations for objects and relations of the KSCGR dataset.
The Kitchen Scene Context based Gesture Recognition (KSCGR) dataset [1] is a fine-grained kitchen action dataset released as a challenge in ICPR 2012. The dataset contains scenes captured by a kinect sensor fixed on the top of the kitchen, providing synchronized color and depth image sequences. Each video is 5 to 10 minutes long, containing 9,000 to 18,000 frames. The organizers of the dataset assigned labels to each frame indicating the type of gesture performed by the actors. There are 8 cooking gestures in the dataset: breaking, mixing, baking, turning, cutting, boiling, seasoning, peeling, and none, where none means that there is no action being performed in the current frame. These gestures are performed in five different menus for cooking eggs in Japan: ham and eggs, omelet, scrambled egg, boiled egg, and kinshi-tamago. A total of 7 different subjects perform each menu. The ground truth data contains the frame id and the action being performed within the frame.
The image below illustrates the sequence of actions in which the dataset is originally annotated.
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS) agreement (DOCFIX 04/2018). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.
[1] A. Shimada, K. Kondo, D. Deguchi, G. Morin, and H. Stern, "Kitchen Scene Context based Gesture Recognition: A contest in ICPR 2012". In Advances in Depth Image Analysis and Applications. Springer, pp. 168-185, 2013.