1. A method of identifying a movement of a human object or a non human object relating to betting or game activity based on self-referential movement data compressed by principal joint variable analysis, the method comprising: receiving, by a classifier of a system, a stream of reference frames from a detector unit, the stream of reference frames comprising a set of self-referential movement data points provided in three dimensions, each self-referential movement data point identifying locations or positions of one or more parts of the human object or the non human object with respect to a reference point on the human object or the non human object with respect to a particular dimension of the three dimensions; determining, by the classifier, that a subset of the set of self-referential movement data points is sufficient to recognize a reference movement relating to betting or game activity; generating, by the classifier, a feature matrix, each row of the feature matrix (i) representative of a particular location or position of the one or more parts of the human object or the non human object, and (ii) having at least three cells, each cell storing a self-referential movement data point of the set of self-referential movement data points corresponding to the particular location or position of the one or more parts of the human object or the non human object in one of the three dimensions; transforming the feature matrix into a compressed feature matrix using a principal joint variable analysis function at a pre-defined variance threshold in combination, collapsing the feature matrix by reducing the three-dimensional data set into a two-dimensional data set or a single-dimensional data set, the compressed feature matrix maintaining only the rows of the feature matrix having a corresponding variance greater than the pre-defined variance threshold; storing, into a database of the system, the compressed feature matrix representative of the reference movement; receiving, by a recognizer of the system, a new stream of frames including new self-referential movement data points, each new self-referential movement data point identifying a location of a part of a new human object or a new non human object with respect to the reference point on the new human object or the new non human object; recognizing, by the recognizer, that the of the new human object or the new non human object corresponds to the reference by comparing only the new self-referential movement data points corresponding to the self-referential movement data points in the compressed feature matrix; and determining that the movement of the new human object or the new non human object corresponds to the reference movement when the new self-referential movement data points corresponding to the compressed feature matrix only vary from the data set of the compressed feature matrix within a pre-defined recognition threshold.