A. Naftel and S. Khalid (UK)
motion data mining, trajectory clustering, classification,
In this paper a new technique is proposed for the
clustering and classification of spatio-temporal object
trajectories extracted from video motion clips. The
trajectories are represented as motion time series and
modelled using Chebyshev polynomial approximations.
Trajectory clustering is then performed to discover
patterns of similar object motion. The coefficients of the
basis functions are used as an input feature vector to a
Self-Organising Map which can learn similarities between
object trajectories in an unsupervised manner. It is shown
that applying machine learning techniques in the
Chebyshev parameter subspace leads to significant
performance gains over previous approaches that encode
trajectories as point-based flow (PBF) vectors.
Experiments using the PETS’04 tracking dataset
demonstrate the effectiveness of clustering in the
parameter subspace and improvements in overall
classification accuracy in comparison with PBF vector
encoding. We also show how this technique can be
further extended to the detection of anomalous motion
paths. Applications to motion data mining and event
detection in video surveillance systems are envisaged.