Understanding mechanical motion: From images to behaviors

We present an algorithm for producing behavior descriptions of planar fixed axes mechanical motions from image sequences using a formal behavior language. The language, which covers the most important class of mechanical motions, symbolically captures the qualitative aspects of objects that translate and rotate along axes that are fixed in space. The algorithm exploits the structure of these motions to robustly recover the objects behaviors. It starts by identifying the independently moving objects, their motion parameters, and their variation with respect to time using normal optical flow analysis, iterative motion segmentation, and motion parameter estimation. It then produces a formal description of their behavior by identifying individual uniform motion events and simultaneous motion changes, and parsing them with a motion grammar. We demonstrate the algorithm on three sets of image sequences: mechanisms, everyday situations, and a robot manipulation scenario.

Authors: T. Dar, L. Joskowicz, E. Rivlin
Year of publication: 1999
Journal: Artificial Intelligence, Volume 112, Issues 1–2, Pages 147-179

Link to publication:


“Working memory”