Tracking an object-grabbing hand using occluded depth reconstruction

We propose a method that is effective in tracking 3D hand poses occluded by a real object. Since existing model-based tracking methods rely only on observed images to estimate hand joints; tracking generally fails when the hand joints are largely invisible. This problem becomes more prevalent when the tracked hand is grabbing an object; as occlusion by the object makes it harder to find a proper correspondence between the hand model and observation. The proposed method utilizes the occluded part of the hand as additional information for model-based tracking. The occluded depth information is reconstructed according to the geometric of the object and model-based tracking is employed based on particle swarm optimization(PSO). We demonstrate that the reconstructed depth information improves the performance of tracking an object-grabbing hand.