Particle Video: Long-Range Motion Estimation Using Point Trajectories

Abstract

This paper describes a new approach to motion estimation in video. We represent video motion using a set of particles. Each particle is an image point sample with a longduration trajectory and other properties. To optimize these particles, we measure point-based matching along the particle trajectories and distortion between the particles. The resulting motion representation is useful for a variety of applications and cannot be directly obtained using existing methods such as optical flow or feature tracking. We demonstrate the algorithm on challenging real-world videos that include complex scene geometry, multiple types of occlusion, regions with low texture, and non-rigid deformations.