  Detection and tracking of dynamic objects is a key feature for autonomous
behavior in a continuously changing environment. With the increasing popularity
and capability of micro aerial vehicles (MAVs) efficient algorithms have to be
utilized to enable multi object tracking on limited hardware and data provided
by lightweight sensors. We present a novel segmentation approach based on a
combination of median filters and an efficient pipeline for detection and
tracking of small objects within sparse point clouds generated by a Velodyne
VLP-16 sensor. We achieve real-time performance on a single core of our MAV
hardware by exploiting the inherent structure of the data. Our approach is
evaluated on simulated and real scans of in- and outdoor environments,
obtaining results comparable to the state of the art. Additionally, we provide
an application for filtering the dynamic and mapping the static part of the
data, generating further insights into the performance of the pipeline on
unlabeled data.
