Yet Another Computer Vision Index To Datasets (YACVID) - Details

Stand: 2017-03-25 000000m 12:32:21 - Overview

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Name (Institute + Shorttitle)Sheffield Kinect Gesture (SKIG) dataset 
Description (include details on usage, files and paper references)The Sheffield Kinect Gesture (SKIG) dataset contains 2160 hand gesture sequences (1080 RGB sequences and 1080 depth sequences) collected from 6 subjects. All these sequences are synchronously captured with a Kinect sensor (including a RGB camera and a depth camera).

This dataset collects 10 categories of hand gestures in total: circle (clockwise), triangle (anti-clockwise), up-down, right-left, wave, "Z", cross, comehere, turn-around, and pat. In the collection process, all these ten categories are performed with three hand postures: fist, index and flat. To increase the diversity, we recorded the sequences under 3 different backgrounds (i.e., wooden board, white plain paper and paper with characters) and 2 illumination conditions (i.e., strong light and poor light).

Consequently, for each subject, we recorded 10(categories) * 3(poses) * 3(backgrounds) * 2(illumination) * 2(RGB and depth) = 360 gesture sequences. (More can be found in Readme.txt)

L. Liu and L. Shao, "Learning Discriminative Representations from RGB-D Video Data", In Proc. International Joint Conference on Artificial Intelligence (IJCAI), Beijing, China, 2013.
 
URL Linkhttp://lshao.staff.shef.ac.uk/data/SheffieldKinectGesture.htm 
Files (#)2160 
References (SKIPPED)
Category (SKIPPED) 
Tags (single words, spaced)gesture, kinect, recognition, human, action, illumination, depth 
Last Changed2017-03-25 
Turing (2.12+3.25=?) :-) 

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