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Method of recognition of human motion, vector sequences and speech

  • US 20030208289A1
  • Filed: 05/01/2003
  • Published: 11/06/2003
  • Est. Priority Date: 05/06/2002
  • Status: Active Grant
First Claim
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1. A method for recognizing an input human motion as most similar to a model human motion, which is a member of a stored collection of model human motions, comprising the steps of:

  • a. Creating a collection of model human motions by measuring and recording the model trajectories of body parts that pertain to human performances of such collection of model human motions;

    b. sampling at a model-rate of sampling, one recorded model trajectories of body parts;

    c. repeating step b each time with another recorded model trajectories of body parts, until all the recorded model trajectories of body parts included in the collection of model human motions, have been sampled;

    d. representing each sample of the recorded model trajectories of body parts by a model vector mrj;

    wherein each component of such model vector is derived from a sample of a recorded model trajectory of one body part;

    e. representing each member of the collection of model human motions by a model vectors sequence Mj=(m1j . . . mrj . . . mqj);

    wherein the subscript q denotes the total number of model vectors in the model vectors sequence Mj;

    wherein the subscript r denotes the location of the model vector mrj within the model vectors sequence Mj;

    wherein the subscript j denotes the serial number of the model vectors sequence Mj within the collection of model vectors sequences {Mj} that represents the corresponding collection of model human motions;

    f. storing in a hash table, the entire collection of model vectors sequences {Mj} by storing each of the model vectors mrj that belongs to the collection of model vectors sequences {Mj}, in a hash table bin whose address is the nearest to the model vector mrj;

    g. Acquiring an input human motion by measuring and recording the input trajectories of body parts that pertain to a human performance of such input human motion;

    h. sampling at an input-rate of sampling, the recorded input trajectories of body parts;

    wherein the input-rate of sampling is set to be substantially different from the model-rate of sampling;

    i. representing each sample of the recorded input trajectories of body parts by an input vector tnk;

    wherein each component of such an input vector is derived from a sample of a recorded input trajectory of the same body part that pertains to the corresponding component of the model vector mrj;

    j. representing the sampled input human motion by an input vectors sequence Tk=(t1k . . . tnk . . . tpk);

    wherein the subscript p denotes the total number of input vectors in the input vectors sequence;

    wherein the subscript n denotes the location of the input vector tnk within the input vectors sequence Tk;

    wherein the subscript k denotes the serial number of the input vectors sequence;

    k. employing an optimal matching algorithm to find the optimal matching score between the input vectors sequence Tk and one model vectors sequence Mj, which is one of the members of the collection of model vectors sequences {Mj};

    l. repeating step k until all the optimal matching scores that pertain to all the model vectors sequences Mj, which are members of the collection of model vectors sequences {Mj} are found;

    m. comparing all the optimal matching scores that pertain to all the model vectors sequences Mj, which are members of the collection of model vectors sequences {Mj} and finding one model vectors sequence Mo that has the highest optimal matching score;

    recognizing the input human motion as most similar to the model human motion that pertains to the model vectors sequence Mo with the highest optimal matching score;

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