Abstract
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.
Original language | English |
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Title of host publication | 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016 |
Publisher | IEEE Signal Processing Society |
Publication date | 23 May 2016 |
Article number | 7477591 |
ISBN (Electronic) | 9781509006410 |
DOIs | |
Publication status | Published - 23 May 2016 |
Externally published | Yes |
Event | IEEE Winter Conference on Applications of Computer Vision, WACV 2016 - Lake Placid, United States Duration: 7 Mar 2016 → 10 Mar 2016 |
Conference
Conference | IEEE Winter Conference on Applications of Computer Vision, WACV 2016 |
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Country/Territory | United States |
City | Lake Placid |
Period | 07/03/2016 → 10/03/2016 |
Series | 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016 |
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Bibliographical note
Publisher Copyright:© 2016 IEEE.