Gesture recognition using depth images

Authors:
Bin Liang
Affiliations:
Charles Sturt University, Wagga Wagga, Australia
Venue:
Proceedings of the 15th ACM on International conference on multimodal interaction
Year:
2013

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Abstract

This work presents an approach for recognizing 3D human gestures by using depth images. The proposed motion trail model (MTM) consists of both motion information and static posture information over the gesture sequence along the xoy-plane. By projecting depth images onto other two planes in 3D space, gestures can be represented with complementary information from additional planes. Accordingly 2D-MTM can be extended into 3D space in addition to the lateral scene parallel to the image plane to generate 3D-MTM. The Histogram of Oriented Gradient (HOG) is then extracted from the proposed 3D-MTM as the feature descriptor. The final recognition of gestures is performed through maximum correlation coefficient. The preliminary results demonstrate the average error rate decreases from 62.80% of baseline method to 21.74% after using the proposed approach on Chalearn gesture dataset.