  The ever-increasing use of artificial intelligence in autonomous systems has
significantly contributed to advance the research on multi-object tracking,
adopted in several real-time applications (e.g., autonomous driving,
surveillance drones, robotics) to localize and follow the trajectory of
multiple objects moving in front of a camera. Current tracking algorithms can
be divided into two main categories: some approaches introduce complex
heuristics and re-identification models to improve the tracking accuracy and
reduce the number of identification switches, without particular attention to
the timing performance, whereas other approaches are aimed at reducing response
times by removing the re-identification phase, thus penalizing the tracking
accuracy. This work proposes a new approach to multi-class object tracking that
allows achieving smaller and more predictable execution times, without
penalizing the tracking performance. The idea is to reduce the problem of
matching predictions with detections into smaller sub-problems by splitting the
Hungarian matrix by class and invoking the second re-identification stage only
when strictly necessary for a smaller number of elements. The proposed solution
was evaluated in complex urban scenarios with several objects of different
types (as cars, trucks, bikes, and pedestrians), showing the effectiveness of
the multi-class approach with respect to state of the art trackers.
