3D Object-grabbing hand tracking based on prior knowledge of grasp

Abstract

We propose a real-time 3D object-grabbing hand tracking system based on the prior knowledge of grasping an object. The problem of tracking a hand interacting with an object is more difficult than the issue of an isolated hand since it requires consideration of occlusion by an object. Most of the previous studies resort to the insufficient data which omit occluded hand data and missed the point that the presence of an object may rather be a constraint on the pose of the hand. In this paper, we focused on the sequence of a hand grabbing an object to utilize prior knowledge of grasp situation. With this assumption, an excluded depth data of the hand occluded by the object was reconstructed with proper depth data and conducted a re-initialization process based on the plausible grasp pose of the human. The effectiveness of the proposed process was verified based on model-based tracker with particle swarm optimization. Quantitative and qualitative experiments demonstrate that the proposed processes can effectively improve the performance of model-based tracker for the object-grabbing hand.