METHOD AND SYSTEM FOR TRACKING A CAD MODEL IN REAL TIME BASED ON PARTICLE FILTERS

1 . A method of tracking a CAD model in real time based on a particle filter, which detects and tracks a real object based on target object recognition data for a digital model designed on CAD executed by a CAD object tracking detection program installed in a user computing device, the method comprising:

acquiring an image captured by photographing a surrounding object;

detecting a real object corresponding to a shape of a target object designed in CAD from a first frame image of the captured image; and

tracking the detected real object in a second frame image of the captured image,

wherein the tracking of the detected real object includes determining a new pose of the real object in the second frame image based on the particle filter with respect to an initial pose of the detected real object.

2 . The method of claim 1 , wherein the detecting of a real object corresponding to the shape of the target object designed in CAD from the first frame image of the captured image includes:

acquiring target object recognition data generated based on a digital model designed for the target object in a computer-aided design program; and

detecting a real object corresponding to the shape of the digital model from the first frame image through the acquired target object recognition data.

3 . The method of claim 2 , wherein the detecting of a real object corresponding to the shape of the digital model from the first frame image through the acquired target object recognition data includes:

detecting the surrounding object in the first frame image;

extracting an edge for each surrounding object; and

detecting, as the real object, the surrounding object in which a sample point in the target object recognition data matches an edge of the surrounding object.

4 . The method of claim 1 , wherein the determining of a new pose of the real object in the second frame image based on the particle filter with respect to the initial pose of the detected real object includes:

determining an initial pose of a real object in the first frame image;

estimating a new pose of the real object in the second frame image based on the initial pose of the real object;

determining sample particles for the estimated new pose; and

determining a new pose of the real object in the second frame image by comparing the determined sample particle with the second frame image.

5 . The method of claim 4 , wherein the estimating of a new pose of the real object in the second frame image based on the initial pose of the real object includes:

estimating the new pose through a constant velocity motion model based on the initial pose of the real object.

6 . The method of claim 5 , wherein the estimating of a new pose of the real object in the second frame image based on the initial pose of the real object further includes:

determining a particle point for each of the estimated new poses;

adding Gaussian noise to the determined particle point;

generating a sample point including the particle point to which the Gaussian noise is added; and

setting a new pose for the generated sample point.

7 . The method of claim 4 , wherein the estimating of a new pose of the real object in the second frame image based on the initial pose of the real object includes:

calculating N number of new poses of the real object by converting the initial pose of the real object into translation parameters (Rx, Tx); and

determining sample particles for the N number of new poses.

8 . The method of claim 7 , wherein the estimating of a new pose of the real object in the second frame image based on the initial pose of the real object further includes:

calculating a correspondence score by comparing the determined N sample particles with a bounding box of the real object of the second frame image;

determining a most ideal sample particle based on the calculated correspondence score; and

determining a pose of the determined ideal sample particle as a new pose of the real object in the second frame image.

9 . The method of claim 8 , wherein the determining of sample particles for the N number of new poses is performed by a first processor (CPU), and the determining of a new pose of the real object in the second frame image by comparing the determined sample particle with the second frame image is performed by a second processor (GPU) in parallel.

10 . The method of claim 1 , further comprising:

matching and displaying augmented content according to the pose of the tracked real object.