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- Title
Improvement of Laplacian eigenmaps for human action recognition.
- Authors
JIN Cheng-bin; CUI Rong-yi; JIN Xiao-feng
- Abstract
This paper presented a method for human action recognition by improving Laplacian eigenmaps algorithm. Firstly, Kinect sensor offered joints data as posture feature. Laplacian eigenmaps algorithm which one of the manifold learning was improved by Levenstein distance mapped the features to two-dimensional space which was the embedding space for behavior recognition. Secondly, the method built a prior model combined embedding space and training data. Finally, by redesigning particle dynamic model and particle observation model, the method employed particle filter algorithm to recognize behavior. The experimental results show that the proposed method can obtain satisfactory results among behaviors existed repetitive actions, shield movement and visible difference in range of movement and speed. Te recognition rate is 92.4%.
- Subjects
PATTERN recognition systems; EIGENANALYSIS; KINECT (Motion sensor); COMPUTER algorithms; MONTE Carlo method; DATA analysis
- Publication
Application Research of Computers / Jisuanji Yingyong Yanjiu, 2014, Vol 31, Issue 12, p3613
- ISSN
1001-3695
- Publication type
Article
- DOI
10.3969/j.issn.1001-3695.2014.12.025