Body-part estimation from Lucas-Kanade tracked Harris points

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.