Abstract
In the context of human action recognition using skeleton data, the 3D trajectories of joint points may be considered as multidimensional time series. The traditional recognition technique in the literature is based on time series dis(similarity) measures (such as Dynamic Time Warping). For these general dis(similarity) measures, k-nearest neighbor algorithms are a natural choice. However, k-NN classifiers are known to be sensitive to noise and outliers. In this paper, a new class of Support Vector Machine that is applicable to trajectory classification, such as action recognition, is developed by incorporating an efficient time-series distances measure into the kernel function. More specifically, the derivative of Dynamic Time Warping (DTW) distance measure is employed as the SVM kernel. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite (PSD) kernels in the SVM formulation. The recognition results of the proposed technique on two action recognition datasets demonstrates the our performance of our methodology compared to the state-of-the-art methods. Remarkably, we obtained 89% accuracy on the well-known MSRAction3D dataset using only 3D trajectories of body joints obtained by Kinect.
Originalsprog | Engelsk |
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Titel | Advances in Artificial Intelligence - 29th Canadian Conference on Artificial Intelligence, Canadian AI 2016, Proceedings |
Redaktører | Richard Khoury, Christopher Drummond |
Antal sider | 12 |
Forlag | Physica-Verlag |
Publikationsdato | 2016 |
Sider | 3-14 |
ISBN (Trykt) | 9783319341101 |
DOI | |
Status | Udgivet - 2016 |
Udgivet eksternt | Ja |
Begivenhed | 29th Canadian Conference on Artificial Intelligence, AI 2016 - Victoria, Canada Varighed: 31 maj 2016 → 3 jun. 2016 |
Konference
Konference | 29th Canadian Conference on Artificial Intelligence, AI 2016 |
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Land/Område | Canada |
By | Victoria |
Periode | 31/05/2016 → 03/06/2016 |
Sponsor | (CAIAC), Canadian Artificial Intelligence Association, Cory Butz (University of Regina) |
Navn | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Vol/bind | 9673 |
ISSN | 0302-9743 |
Bibliografisk note
Publisher Copyright:© Springer International Publishing Switzerland 2016.