Motion Data Mining and Activity Recognition

Techniques for understanding video object motion activity are becoming increasingly important with the widespread adoption of CCTV surveillance systems. Motion trajectories provide rich spatiotemporal information about an object s activity. This book presents a novel technique for clustering and classification of object trajectory-based video motion clips using basis function approximation. In the proposed motion learning system, trajectories are treated as time series and modelled using orthogonal basis function representation. A novel framework (Iterative HSACT-SOM) is proposed that exploits the chosen feature subspace and performs efficient and effective motion learning in the presence of significant number of anomalies in training data. A novel modelling technique, referred to as m-Mediods, is proposed that models the class containing n members with m Mediods. Once the m- Mediods based model for all the classes have been learnt, the classification of new trajectories and anomaly detection can be performed by checking the closeness of said trajectory to the models of known classes using an agglomerative approach.