Unsupervised Learning of Motion Patterns
Thomas Guthier, Julian P. Eggert, Volker Willert · The European Symposium on Artificial Neural Networks · 2012
Neurophysiological findings suggest that the visual cortex of mammals contains neural populations that are sensitive to specific motion patterns. In this paper, we present a new method to learn such patterns in an unsupervised way. To represent motion, dense optical flow fields of videos containing humans performing several actions like walking and running are estimated. We introduce VNMF, an extension of the translation invariant NMF that works on flow fields, along with a new energy term that enforces parts-basedness. VNMF incorporates three principles found in neural motion processing: Sparsity, non-negativity and direction selectivity. We find that the extracted motion patterns are shaped like body parts, which supports the idea that the representation of biological motion is directly linked to the shape of an object.