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arXiv · 1501.05432

Point Context: An Effective Shape Descriptor for RST-invariant Trajectory Recognition

Xingyu Wu·Xia Mao·Lijiang Chen·Yuli Xue·Angelo Compare
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Abstract

Motion trajectory recognition is important for characterizing the moving property of an object. The speed and accuracy of trajectory recognition rely on a compact and discriminative feature representation, and the situations of varying rotation, scaling and translation has to be specially considered. In this paper we propose a novel feature extraction method for trajectories. Firstly a trajectory is represented by a proposed point context, which is a rotation-scale-translation (RST) invariant shape descriptor with a flexible tradeoff between computational complexity and discrimination, yet we prove that it is a complete shape descriptor. Secondly, the shape context is nonlinearly mapped to a subspace by kernel nonparametric discriminant analysis (KNDA) to get a compact feature representation, and thus a trajectory is projected to a single point in a low-dimensional feature space. Experimental results show that, the proposed trajectory feature shows encouraging improvement than state-of-art methods.

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Xingyu Wu, Xia Mao, Lijiang Chen, Yuli Xue, Angelo Compare. 2015-01-22. Point Context: An Effective Shape Descriptor for RST-invariant Trajectory Recognition. https://doi.org/10.1007/s10851-016-0648-6

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