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|Title:||Structured streaming skeleton - a new feature for online human gesture recognition|
|Citation:||ACM Transactions on Multimedia Computing Communications and Applications, 2014; 11(1):22-1-22-18|
|Publisher:||Association for Computing Machinery|
|Xin Zhao, Xue Li, Chaoyi Pang, Quan Z. Sheng, Sen Wang, Mao Ye|
|Abstract:||Online human gesture recognition has a wide range of applications in computer vision, especially in human-computer interaction applications. The recent introduction of cost-effective depth cameras brings a new trend of research on body-movement gesture recognition. However, there are two major challenges: (i) how to continuously detect gestures from unsegmented streams, and (ii) how to differentiate different styles of the same gesture from other types of gestures. In this article, we solve these two problems with a new effective and efficient feature extraction method—Structured Streaming Skeleton (SSS)—which uses a dynamic matching approach to construct a feature vector for each frame. Our comprehensive experiments on MSRC-12 Kinect Gesture, Huawei/3DLife-2013, and MSR-Action3D datasets have demonstrated superior performances than the state-of-the-art approaches. We also demonstrate model selection based on the proposed SSS feature, where the classifier of squared loss regression with l2,1 norm regularization is a recommended classifier for best performance.|
|Keywords:||Scene Analysis;motion; implementation; interactive systems|
|Rights:||© 2014 ACM|
|Appears in Collections:||Aurora harvest 7|
Computer Science publications
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