An Image-Constrained Particle Filter for 3D Human Motion Tracking

Tracking 3D human motion from monocular video sequences has aroused great interest in recent years. Among these human motion tracking methods, the particle filter is considered as an effective approach. However, the current approaches based on particle filter still have some limitation such as many particles are obviously not consistent with the observed image due to they are independent of the image information. In this paper, we present an image-constrained particle filter approach to track 3D human motion from monocular video clips with the assistance of a pre-captured motion library. We propose two novel particle filtering criteria and design a hierarchical likelihood function. The top layer of the function consists of the particle filtering criteria, and the bottom layer consists of the likelihood functions based on image contours and edge features. We remove those particles that do not match the image significantly at the top level, and the remaining particles are evaluated using the underlying likelihood function. The experimental results show that our method can effectively improve the accuracy of motion tracking and constrain the estimation of human body position.