Motion trajectory clustering for video retrieval using spatio-temporal approximations

A new technique is proposed for clustering and similarity retrieval of video motion clips based on spatio-temporal object trajectories. The trajectories are treated as motion time series and modelled using orthogonal basis polynomial approximations. Trajectory clustering is then carried out to discover patterns of similar object motion behaviour. The coefficients of the basis functions are used as input feature vectors to a Self-Organising Map which can learn similarities between object trajectories in an unsupervised manner. Clustering in the basis coefficient space leads to efficiency gains over existing approaches that encode trajectories as point-based flow vectors. Experiments on pedestrian motion data gathered from video surveillance demonstrate the effectiveness of our approach, Applications to motion data mining in video surveillance databases are envisaged.