  We present a novel method, called CenterPoly, for real-time instance
segmentation using bounding polygons. We apply it to detect road users in dense
urban environments, making it suitable for applications in intelligent
transportation systems like automated vehicles. CenterPoly detects objects by
their center keypoint while predicting a fixed number of polygon vertices for
each object, thus performing detection and segmentation in parallel. Most of
the network parameters are shared by the network heads, making it fast and
lightweight enough to run at real-time speed. To properly convert mask
ground-truth to polygon ground-truth, we designed a vertex selection strategy
to facilitate the learning of the polygons. Additionally, to better segment
overlapping objects in dense urban scenes, we also train a relative depth
branch to determine which instances are closer and which are further, using
available weak annotations. We propose several models with different backbones
to show the possible speed / accuracy trade-offs. The models were trained and
evaluated on Cityscapes, KITTI and IDD and the results are reported on their
public benchmark, which are state-of-the-art at real-time speeds. Code is
available at https://github.com/hu64/CenterPoly
