Claims:
1. A processor implemented method, comprising:
obtaining, by one or more hardware processors, one or more input data comprising skeletal data, depth data and red, green, and blue (RGB) data pertaining to at least one user at one or more time stamps, wherein said one or more input data comprises three dimensional (3D) coordinates pertaining to one or more skeleton joints;
estimating, using said one or more input data, by said one or more hardware processors, in a first set of frames, one or more body joint locations and a body segment length of one or more body segments connected to corresponding skeleton joints to obtain (i) an estimate of one or more body joint locations and (ii) an estimate of one or more body segment lengths;
iteratively identifying, by said one or more hardware processors, one or more probable correct body joint locations in a bounded neighborhood around said one or more body joint locations that are previously obtained, wherein said one or more probable correct body joint locations are iteratively identified based on said depth data and said RGB data;
performing, by said one or more hardware processors, a comparison of (i) a body segment length associated with each of said one or more probable correct body joint locations and (ii) a reference length;
identifying, by said one or more hardware processors, at least a subset of said one or more probable correct body joint locations as one or more candidate body joint locations based on said comparison;
determining a physical orientation of each body segment pertaining to each of said one or more candidate body joint locations by segmenting one or more 3D coordinates of each body segment based on said depth data and performing an analysis on each segmented 3D coordinate; and
identifying, from said one or more candidate body joint locations, a body joint location based on a minimal deviation in direction from said physical orientation of a corresponding body segment along with a feature descriptor of said RGB data and said depth data, wherein said minimal deviation is based on said depth data, and wherein said minimal deviation is based on one or more actions performed by said user.

2. The processor implemented method of claim 1, wherein identifying, by said one or more hardware processors, at least a subset of said one or more probable correct body joint locations as one or more candidate body joint locations is based on said comparison resulting in a minimal body segment length variation.

3. The processor implemented method of claim 1, wherein a search region is defined for identifying said one or more probable correct body joint locations using a radius equivalent to a displacement of a corresponding skeleton joint.

4. The processor implemented method of claim 1, wherein said estimate of said one or more body segment lengths is based on a Euclidean distance between adjacent skeleton joints.

5. The processor implemented method of claim 1, wherein said reference length is derived based on said estimate of said one or more body segment lengths.

6. A system comprising:
a memory storing instructions;
one or more communication interfaces; and
one or more hardware processors coupled to said memory using said one or more communication interfaces, wherein said one or more hardware processors are configured by said instructions to:
obtain one or more input data comprising skeletal data, depth data and red, green, and blue (RGB) data pertaining to at least one user at one or more time stamps, wherein said one or more input data comprising three dimensional (3D) coordinates pertaining to one or more skeleton joints,
estimate, using said one or more input data, one or more body joint locations and a body segment length of one or more body segments connected to corresponding skeleton joints to obtain (i) an estimate of one or more body joint locations and (ii) an estimate of one or more body segment lengths,
identify one or more probable correct body joint locations in a bounded neighborhood around said one or more body joint locations that are previously obtained, wherein said one or more probable correct body joint locations are iteratively identified based on said depth data and said RGB data,
perform a comparison of (i) a body segment length associated with each of said one or more probable correct body joint locations and (ii) a reference length;
identify at least a subset of said one or more probable correct body joint locations as one or more candidate body joint locations based on said comparison,
determine a physical orientation of each body segment pertaining to each of said one or more candidate body joint locations by segmenting one or more 3D coordinates of each body segment based on said depth data and performing an analysis on each segmented 3D coordinate, and
identify, from said one or more candidate body joint locations, a body joint location based on a minimal deviation in direction from said physical orientation of a corresponding body segment along with a feature descriptor of said RGB data and said depth data, wherein said minimal deviation is based on said depth data, and wherein said minimal deviation is based on one or more actions performed by said user.

7. The system of claim 6, wherein at least a subset of said one or more probable correct body joint locations are identified as one or more candidate body joint locations based on said comparison resulting in a minimal body segment length variation.

8. The system of claim 6, wherein a search region is defined for identifying said one or more probable correct body joint locations based on a radius equivalent to a displacement of a corresponding skeleton joint.

9. The system of claim 6, wherein said estimate of said one or more body segment lengths is based on a Euclidean distance between adjacent skeleton joints.

10. The system of claim 6, wherein said reference length is derived based on said estimate of said one or more estimated body segment lengths.
, Description:As Attached