TUM Kitchen is an action recognition dataset that covers daily activities in the kitchen. The dataset contains 20 video sequences shot by 4 cameras with overlapping shooting angles. The cameras capture the scene from 4 angles at 25 fps, and the resolution of each RGB frame is 384×288 pixels. The data are annotated as left hand, right hand, and human torso.