Continuous interaction learning and detection in real-time

An apparatus to process training samples, comprising:

• a partition module to partition a plurality of training samples into a sequential list of centroids;
• a filter to remove one or more repeating centroids in the sequential list of centroids to obtain a reduced list of centroids;
• and
• a parameter module to generate a set of Hidden Markov Model (HMM) parameters based on the reduced list of centroids.

A method to process training samples, comprising:

• partitioning a plurality of training samples into a sequential list of centroids;
• removing one or more repeating centroids in the sequential list of centroids to obtain a reduced list of centroids;
• and
• generating a set of Hidden Markov Model (HMM) parameters based on the reduced list of centroids.

A method to process detection samples, comprising:

• partitioning a plurality of detection samples into a sequential list of centroids;
• removing one or more repeating centroids in the sequential list of centroids to obtain a reduced list of centroids;
• and
• using the reduced list of centroids to determine a match probability for the plurality of detection samples against a set of Hidden Markov Model (HMM) parameters associated with a training session.

At least one computer readable storage medium comprising a set of instructions which, if executed by a computing device, cause the computing device to:

• partition a plurality of detection samples into a sequential list of centroids;
• remove one or more repeating centroids in the sequential list of centroids to obtain a reduced list of centroids;
• and
• use the reduced list of centroids to determine a match probability for the plurality of detection samples against a set of Hidden Markov Model (HMM) parameters associated with a training session.