Monocular Tracking 3D People with Back Constrained Scaled Gaussian Process Latent Variable Models

Tracking 3D people from monocular video is often poorly constrained. To mitigate this problem, prior information can be exploited. In learning the prior stage, most algorithms think representing high-dimensional pose space in low-dimensional space as dimension reduction procedure, without considering the geometrical relation or time correlation in pose space. Therefore, the prior loses physical constrains in pose space. In this paper, the back constrained scaled Gaussian process latent variable model (back constrained SGPLVM), a novel dimensionality reduction method for learning human poses is proposed. The low-dimensional latent space is optimized for preserving geometrical relation or time correlation in high-dimensional pose space. The learned latent space is a smooth manifold. The smooth latent space will be satisfactory state space for tracking to avoid searching in high-dimensional pose directly. Experiment demonstrates that the back constrained SGPLVM integrated with particle filtering framework can track 3D people accurately and robustly, despite weak and noisy image measurements.