| Motion trajectory can be an informative and descriptive clue that is
suitable for the characterization of motion. Studying motion trajectory
for effective motion description and recognition is important in
many applications. For instance, motion trajectory can play an important
role in the representation, recognition and learning of most
long-term human or robot actions, behaviors and activities. However,
effective trajectory descriptors are lacking and most reported
work just uses motion trajectory in its raw data form. In this paper,
we propose a novel motion trajectory signature descriptor and study
its rich descriptive invariants which benefit effective motion trajectory
recognition. These invariants are key measures of the flexibility
and effectiveness of a descriptor. Substantial descriptive invariants
can be deduced from the proposed trajectory signature, which is attributed
to the computational locality of the signature components.
We first present the signature definition and its robust implementation.
Then the signature’s invariants are elaborated. A non-linear
inter-signature matching algorithm is developed to measure the signature’s
similarity for trajectory recognition. Experiments are conducted
to recognize human sign language, in which both synthetic
and real data are used to verify the signature’s invariants, and to illustrate
the effectiveness in the signature recognition. |
