Collaborative Differential Evolution Filtering for Tracking Hand-Object Interactions

Human hands engage in interactive activities in many practical working scenarios, among which the interactions between human hands and objects are the most common. Tracking the movement of the human hand during hand-object interactions is an important research task that is also challenging due to the high-dimensionality and occlusions. In this paper, we track hand-object interactions from depth observations with a model-based method. To overcome the difficulties of optimum searching in the hand-object high-dimensional space, we propose a new algorithm - collaborative differential evolution filtering (CoDEF) - for tracking hand-object interactions. The proposed CoDEF algorithm integrates the differential evolution (DE) algorithm into a particle filtering (PF) framework to accelerate the convergence of particles. Particles are driven to the regions with a high probability by optimizing the matching error under the current observation with DE. To decompose the state space and decrease the complexity of optimum searching, CoDEF tracks the movement of the hand and object by using two collaborative trackers. Based on the proposed CoDEF algorithm, we develop a model-based tracking system with 3D graphic techniques. According to the experimental results, the proposed CoDEF algorithm can achieve robust tracking of hand-object interactions using fewer particles.