Skeleton estimation from single-camera grayscale images is generally accomplished using model-based
techniques. Multiple cameras are sometimes used; however, skeletal points extracted from a single subject using
multiple images are usually too sparse to be helpful for localizing body parts. For this project, we use a single viewpoint
without any model-based assumptions to identify a central source of motion, the body, and its associated extremities.
Harris points are tracked using Lucas-Kanade refinement with a weighted kernel found from expectation maximization.
The algorithm tracks key image points and trajectories and re-represents them as complex vectors describing the motion
of a specific body part. Normalized correlation is calculated from these vectors to form a matrix of graph edge weights,
which is subsequently partitioned using a graph-cut algorithm to identify dependent trajectories. The resulting Harris
points are clustered into rigid component centroids using mean shift, and the extremity centroids are connected to their
nearest body centroid to complete the body-part estimation. We collected ground truth labels from seven participants for
body parts that are compared to the clusters given by our algorithm.