Visual trigger templates for knowledge-based indexing

We present an application to create binary Visual Trigger Templates (VTT) for automatic video indexing. Our approach is based on the observation that videos captured with fixed cameras have specific structures that depend on world constraints. Our system allows a user to graphically represent such constraints to automatically recognize simple actions or events. VTTs are constructed by manually drawing rectangles to define trigger spaces: when elements (e.g., a hand, a face) move inside the trigger spaces defined by the user, actions are recognized. For example, a user can define a raise hand action by drawing two rectangles: one for the face and one for the hand. Our approach uses motion, skin, and face detection algorithms. We present experiments on the PETS-ICVS dataset and on our own dataset to demonstrate that our system constitutes a simple but powerful mechanism for meeting video indexing.