  This paper presents an approach for tracking in a surveillance scenario.
Typical aspects for this scenario are a 24/7 operation with a static camera
mounted above the height of a human with many objects or people. The Multiple
Object Tracking Benchmark 20 (MOT20) reflects this scenario best. We can show
that our approach is real-time capable on this benchmark and outperforms all
other real-time capable approaches in HOTA, MOTA, and IDF1. We achieve this by
contributing a fast Siamese network reformulated for linear runtime (instead of
quadratic) to generate fingerprints from detections. Thus, it is possible to
associate the detections to Kalman filters based on multiple tracking specific
ratings: Cosine similarity of fingerprints, Intersection over Union, and pixel
distance ratio in the image.
