1. A computer implemented method for identifying content relevance in a video stream, said method comprising: acquiring at a computer, video data from a video camera: mapping extracted features of said acquired video data to a feature space to obtain a feature representation of said video data; assigning said acquired video data, with a classifier, to at least one action class based on said feature representation of said video data, said classifier comprising at least one of a support vector machine, a neural network, a decision tree, an expectation-maximization algorithm, and a k-nearest neighbor clustering algorithm; and determining a relevance of said acquired video data based on said at least one action class assigned, wherein determining a relevance of said acquired video data based on said at least one action class assigned comprises: assigning said acquired video data a classification confidence score; and converting said classification confidence score to a relevance score.