Title :
One shot learning gesture recognition from RGBD images
Author :
Wu, Di ; Zhu, Fan ; Shao, Ling
Author_Institution :
Univ. of Sheffield, Sheffield, UK
Abstract :
We present a system to classify the gesture from only one learning example. The inputs are duo-modality, i.e. RGB and depth sensor from Kinect. Our system performs morphological denoising on depth images and automatically segments the temporal boundaries. Features are extracted based on Extended-Motion-History-Image (Extended-MHI) and the Multi-view Spectral Embedding (MSE) algorithm is used to fuse duo modalities in a physically meaningful manner. Our approach achieves less than 0.3 in Levenshtein distance in CHALEARN Gesture Challenge validation batches [1].
Keywords :
feature extraction; gesture recognition; image classification; image colour analysis; image denoising; image segmentation; learning (artificial intelligence); spatial variables measurement; CHALEARN gesture challenge validation batch; Kinect RGB sensor; Kinect depth sensor; Levenshtein distance; MSE algorithm; RGBD images; automatic temporal boundary segmentation; depth image denoising; duo-modality; extended-MHI algorithm; extended-motion-history-image algorithm; feature extraction; gesture classification; morphological denoising; multiview spectral embedding algorithm; one shot learning gesture recognition; Cameras; Image segmentation; Motion segmentation; Noise; Noise reduction; Training; Vectors;
Conference_Titel :
Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on
Conference_Location :
Providence, RI
Print_ISBN :
978-1-4673-1611-8
Electronic_ISBN :
2160-7508
DOI :
10.1109/CVPRW.2012.6239179
