1. A computer-implemented method for recognizing and analyzing activities, comprising: learning, via one or more processors, parameters of a classifier in a training operation based on feature vectors and activity elements corresponding to objects during one or more annotated example executions, wherein the classifier represents activity elements at multiple levels of detail; extracting feature vectors corresponding to objects that interact during a test execution; and determining, based on the extracted feature vectors and using the classifier, activity elements associated with the one or more objects, at the multiple levels of detail.