  In this paper, we aim at improving the tracking of road users in urban
scenes. We present a constraint programming (CP) approach for the data
association phase found in the tracking-by-detection paradigm of the multiple
object tracking (MOT) problem. Such an approach can solve the data association
problem more efficiently than graph-based methods and can handle better the
combinatorial explosion occurring when multiple frames are analyzed. Because
our focus is on the data association problem, our MOT method only uses simple
image features, which are the center position and color of detections for each
frame. Constraints are defined on these two features and on the general MOT
problem. For example, we enforce color appearance preservation over
trajectories and constrain the extent of motion between frames. Filtering
layers are used in order to eliminate detection candidates before using CP and
to remove dummy trajectories produced by the CP solver. Our proposed method was
tested on a motorized vehicles tracking dataset and produces results that
outperform the top methods of the UA-DETRAC benchmark.
