Articulated Human Motion Tracking Using Sequential Immune Genetic Algorithm

We formulate human motion tracking as a high-dimensional constrained optimization problem. A novel generative method is proposed for human motion tracking in the framework of evolutionary computation. The main contribution is that we introduce immune genetic algorithm (IGA) for pose optimization in latent space of humanmotion. Firstly, we performhumanmotion analysis in the learnt latent space of human motion. As the latent space is low dimensional and contents the prior knowledge of human motion, it makes pose analysis more efficient and accurate. Then, in the search strategy, we apply IGA for pose optimization. Compared with genetic algorithm and other evolutionary methods, its main advantage is the ability to use the prior knowledge of human motion. We design an IGA-based method to estimate human pose from static images for initialization of motion tracking. Andwe propose a sequential IGA (S-IGA) algorithm formotion tracking by incorporating the temporal continuity information into the traditional IGA. Experimental results on different videos of different motion types show that our IGA-based pose estimation method can be used for initialization ofmotion tracking.The S-IGA-basedmotion trackingmethod can achieve accurate and stable tracking of 3D human motion.