Monocular Human Motion Tracking in Latent Space based on Sequential Immune Genetic Algorithm

Abstract

In this paper, we formulate human motion tracking as a high dimensional constrained dynamic optimization problem. A novel generative method, called Sequential Immune Genetic Algorithm, is proposed for human motion tracking. The main contribution is that we introduce immune genetic algorithm (IGA) for pose optimization in latent space of human motion. As the latent space is low-dimensional and contains the prior knowledge of human motion, it makes pose analysis more efficient and accurate. We apply IGA for pose optimization. Compared with GA and other evolutionary methods, its main advantage is the ability to use the prior knowledge of human motion. As motion tracking is a dynamic optimization problem, we incorporate the temporal continuity information into the traditional IGA and propose a sequential IGA (S-IGA) algorithm. We demonstrate our methods on different videos of different motion types. Experimental results show that the S-IGA motion tracking method can achieve accurate and stable tracking of 3D human motion.