10. A system for using self-referential movement data compressed by principal joint variable analysis to identify a movement of a human object or a non human object relating to betting or game activity, the system comprising: a classifier configured to receive 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 a body of the human object or the non human object with respect to a reference point on the body of the human object or the non human object with respect to a particular dimension of the three dimensions; the classifier configured to determine 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; the classifier configured to generate 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 body, 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 body in one of the three dimensions; the classifier configured to transform the feature matrix into a compressed feature matrix using a principal joint variable analysis function at a pre-defined variance threshold, 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; a data storage configured to store the compressed feature matrix representative of the reference movement; and a recognizer configured to receive 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 body of a new human object or the new non human object with respect to the reference point on the body of the new human object or the new non human object and recognizing 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.
10. A system for using self-referential movement data compressed by principal joint variable analysis to identify a movement of a human object or a non human object relating to betting or game activity, the system comprising: a classifier configured to receive 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 a body of the human object or the non human object with respect to a reference point on the body of the human object or the non human object with respect to a particular dimension of the three dimensions; the classifier configured to determine 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; the classifier configured to generate 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 body, 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 body in one of the three dimensions; the classifier configured to transform the feature matrix into a compressed feature matrix using a principal joint variable analysis function at a pre-defined variance threshold, 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; a data storage configured to store the compressed feature matrix representative of the reference movement; and a recognizer configured to receive 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 body of a new human object or the new non human object with respect to the reference point on the body of the new human object or the new non human object and recognizing 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. 17. The system of claim 10 , wherein the recognizer is further configured to perform a slow and fast motion vector compression function to extract one or more additional mid-frames between first frame and the second frame by plotting the one or more additional mid-frames using a 4 th order polynomial function applied to the feature matrix, the one or more additional mid-frames being extracted to provide a sufficient number of frames to detect or recognize the reference movement and added to the feature matrix prior to generation of the compressed feature matrix.