  We present a method to perform online Multiple Object Tracking (MOT) of known
object categories in monocular video data. Current Tracking-by-Detection MOT
approaches build on top of 2D bounding box detections. In contrast, we exploit
state-of-the-art instance aware semantic segmentation techniques to compute 2D
shape representations of target objects in each frame. We predict position and
shape of segmented instances in subsequent frames by exploiting optical flow
cues. We define an affinity matrix between instances of subsequent frames which
reflects locality and visual similarity. The instance association is solved by
applying the Hungarian method. We evaluate different configurations of our
algorithm using the MOT 2D 2015 train dataset. The evaluation shows that our
tracking approach is able to track objects with high relative motions. In
addition, we provide results of our approach on the MOT 2D 2015 test set for
comparison with previous works. We achieve a MOTA score of 32.1.
