This paper presents a low cost real-time alternative to available commercial human motion capture systems. First, a set of distinguishable markers are placed on several human body landmarks and the scene is captured by a number of calibrated and synchronized cameras. In order to establish a physical relation among markers, a human body model (HBM) is defined. Markers are detected on all camera views and delivered as the input of an annealed particle filter scheme where every particle encodes an instance of the pose of the HBM to be estimated. Likelihood between particles and input data is performed through the generalized symmetric epipolar distance and kinematic constrains are enforced in the propagation step towards avoiding impossible poses. Tests over the HumanEva annotated dataset yield quantitative results showing the effectiveness of the proposed algorithm. Results over sequences involving fast and complex motions are also presented.

Peer Reviewed