  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.

Computer Vision and Pattern Recognition