The application uses the approach introduced in paper Covariance Tracking using Model Update Based on Means on Riemannian Manifolds , F.Porikli, O.Tuzel, P.Meer.

The tracking is based on:

1) initializing the target region

2) constructing the Feature Vectors for each pixel in the target region (first frame)

3) forming the Covariance Matrix using feature vectors generated in step 1

4) determining the candidate regions for the following frames and constructing the covariance matrices of these regions

5) finding the minimum covariance-distanced region from these candidate region

6) assign this region as the estimated region

-The application uses the approach introduced in paper　Covariance Tracking using Model Update Based on Means on Riemannian Manifolds , F.Porikli, O.Tuzel, P.Meer.

The tracking is based on:

1) initializing the target region

2) constructing the Feature Vectors for each pixel in the target region (first frame)

3) forming the Covariance Matrix using feature vectors generated in step 1

4) determining the candidate regions for the following frames and constructing the covariance matrices of these regions

5) finding the minimum covariance-distanced region from these candidate region

6) assign this region as the estimated region