  Soft smooth particles in silo discharge show peculiar characteristics,
including, for example, non-permanent clogging and intermittent flow. This
paper describes a study of soft, low-frictional hydro\-gel spheres in a
quasi-2D silo. We enforce a more competitive behavior of these spheres during
their discharge by placing an obstacle in front of the outlet of the silo.
High-speed optical imaging is used to capture the process of discharge. All
particles in the field of view are identified and tracked by means of machine
learning software using a MASK R-CNN algorithm. With particle tracking
velocimetry (PTV), the fields of velocity, egress time, packing fraction, and
kinetic stress are analysed in this study. While in pedestrian dynamics, the
placement of an obstacle in front of a narrow gate may reduce the stress near
the exit and enable a more efficient egress, the effect is opposite for our
soft grains. Placing an obstacle above the orifice always led to a reduction of
the flow rates, in some cases even to increased clogging probabilities.
