Kernel-based representation for 2D/3D motion trajectory retrieval and classification

This paper proposes a novel kernel-space representation for motion trajectories. Contrasted to most trajectory representation methods in the literature, our method is more generic in the sense that it can be applied to either 2D or 3D trajectories. In the proposed method, a trajectory is firstly projected by the Kernel Principal Component Analysis (KPCA) which can be considered as an implicit mapping to a much higher-dimensional feature space. The high dimensionality can effectively improve the accuracy in recognizing motion trajectories. Then, Nonparametric Discriminant Analysis (NDA) is used to extract the most discriminative features from the KPCA feature space. The synergistic effect of KPCA and NDA leads to better class separability and makes the proposed trajectory representation a more powerful discriminator. The experimental validation of the proposed method is conducted on the Australian Sign Language (ASL) data set. The results show that our method performs significantly better, in both trajectory classification and retrieval, than the state-of-the-art techniques.

► A generic representation for multi-dimensional trajectory with maximized discriminability in kernel space.
► A new method for exploiting the richer information contained in 3D trajectory data.
► Demonstrate its superior performance in trajectory-based classification and retrieval via experiments on a standard data set.