Abstract:
Tracking people by their appearance across disjoint
camera views is challenging since appearance may
vary significantly across such views. This problem has
been tackled in the past by computing intensity transfer
functions between each camera pair during an initial
training stage. However, in real-life situations,
intensity transfer functions depend not only on the
camera pair, but also on the actual illumination at
pixel-wise resolution and may prove impractical to
estimate to a satisfactory extent. For this reason, in
this paper we propose an appearance representation
for people tracking capable of coping with the typical
illumination changes occurring in a surveillance
scenario. Our appearance representation is based on
an online K-means color clustering algorithm, a fixed,
data-dependent intensity transformation, and the
incremental use of frames. Moreover, a similarity
measurement is proposed to match the appearance
representations of any two given moving objects along
sequences of frames. Experimental results presented in
this paper show that the proposed methods provides a
viable while effective approach for tracking people
across disjoint camera views in typical surveillance
scenarios.
