Motion tracking as spatio-temporal motion boundary detection
The purpose of this study is to investigate tracking of moving objects in a sequence of images by detecting the surface generated by motion boundaries in the spacetime domain. Estimation of this spatio-temporal surface is formulated as a Bayesian image partitioning problem. Minimization of the resulting energy functional seeks a solution biased toward smooth closed surfaces which coincide with motion boundaries, have small area, and partition the image into regions of contrasting motion activity. The EulerLagrange partial differential equations of minimization are expressed as level set evolution equations to obtain a topology independent and numerically stable algorithm. The formulation does not require estimation of the image motion field or assume a known background. It allows multiple non-simultaneous independent motions to occur and can account for camera motion without prior estimation of this motion. The analysis assumes short-range image motion. With moving cameras, it assumes that this short-range motion varies smoothly everywhere except across motion boundaries.
tracking level sets motion analysis image sequence analysis