Share Email Print

Proceedings Paper

Body-part estimation from Lucas-Kanade tracked Harris points
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

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.

Paper Details

Date Published: 19 February 2013
PDF: 8 pages
Proc. SPIE 8655, Image Processing: Algorithms and Systems XI, 865506 (19 February 2013); doi: 10.1117/12.2005713
Show Author Affiliations
Vladimir Pribula, Rochester Institute of Technology (United States)
Roxanne L. Canosa, Rochester Institute of Technology (United States)

Published in SPIE Proceedings Vol. 8655:
Image Processing: Algorithms and Systems XI
Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev, Editor(s)

© SPIE. Terms of Use
Back to Top