Design of a morphological moving object signature and application to human identification

Many computer vision systems try to infer semantic information
about a video scene content by looking at the time series
of the silhouettes of the moving objects. This paper proposes
a new inter-frame feature set (signature) based on piecewise
surfacic descriptions of binary silhouettes. It captures the dynamics
of moving objects and compacts it into a robust set of
features suitable for classification. To assess its ability to represent
motion information, we use it to build a complete gait
recognition algorithm that we test on a database of 21 different
subjects. To highlight the efficiency of our signature,
we use frontal views instead of side views of persons, which
is less discussed in literature and is considered to be harder
as the movement of legs is not visible. In that context, the
high recognition rates obtained (over 95% of correct identifications)
proves that our signature is appropriate to describe
moving objects.