  In this paper, we present a new method for detecting road users in an urban
environment which leads to an improvement in multiple object tracking. Our
method takes as an input a foreground image and improves the object detection
and segmentation. This new image can be used as an input to trackers that use
foreground blobs from background subtraction. The first step is to create
foreground images for all the frames in an urban video. Then, starting from the
original blobs of the foreground image, we merge the blobs that are close to
one another and that have similar optical flow. The next step is extracting the
edges of the different objects to detect multiple objects that might be very
close (and be merged in the same blob) and to adjust the size of the original
blobs. At the same time, we use the optical flow to detect occlusion of objects
that are moving in opposite directions. Finally, we make a decision on which
information we keep in order to construct a new foreground image with blobs
that can be used for tracking. The system is validated on four videos of an
urban traffic dataset. Our method improves the recall and precision metrics for
the object detection task compared to the vanilla background subtraction method
and improves the CLEAR MOT metrics in the tracking tasks for most videos.
