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2016 IEEE Winter Conference on Applications of Computer Vision (WACV) (2016)
Lake Placid, NY, USA
March 7, 2016 to March 10, 2016
ISBN: 978-1-5090-0640-3
pp: 1-7
Mohammad Ali Bagheri , Dalhousie University, University of Lar
Qigang Gao , Dalhousie University
Sergio Escalera , Universitat de Barcelona
Despite the outperformance of Support Vector Machine (SVM) on many practical classification problems, the algorithm is not directly applicable to multi-dimensional trajectories having different lengths. In this paper, a new class of SVM that is applicable to trajectory classification, such as action recognition, is developed by incorporating two efficient time-series distances measures into the kernel function. Dynamic Time Warping and Longest Common Subsequence distance measures along with their derivatives are employed as the SVM kernel. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite kernels in the SVM formulation. The proposed method is employed for a challenging classification problem: action recognition by depth cameras using only skeleton data; and evaluated on three benchmark action datasets. Experimental results demonstrate the outperformance of our methodology compared to the state-of-the-art on the considered datasets.
Kernel, Support vector machines, Time series analysis, Trajectory, Visualization, Time measurement, Skeleton,
Mohammad Ali Bagheri, Qigang Gao, Sergio Escalera, "Support vector machines with time series distance kernels for action classification", 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), vol. 00, no. , pp. 1-7, 2016, doi:10.1109/WACV.2016.7477591
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