Learning spatio-temporal relational structures

We introduce a rule-based approach for learning and recognition of complex actions in terms of spatio-temporal attributes of primitive event sequences . During learning, spatio-temporal decision trees are generated which satis fy relational constraints of the training data. The resulting rules are use d to classify new dynamic pattern fragments, and general heuristic rules ar e used to combine classification evidences of different pattern fragments.