  This paper presents a real-time method to detect and track multiple mobile
ground robots using event cameras. The method uses density-based spatial
clustering of applications with noise (DBSCAN) to detect the robots and a
single k-dimensional (k-d) tree to accurately keep track of them as they move
in an indoor arena. Robust detections and tracks are maintained in the face of
event camera noise and lack of events (due to robots moving slowly or
stopping). An off-the-shelf RGB camera-based tracking system was used to
provide ground truth. Experiments including up to 4 robots are performed to
study the effect of i) varying DBSCAN parameters, ii) the event accumulation
time, iii) the number of robots in the arena, and iv) the speed of the robots
on the detection and tracking performance. The experimental results showed 100%
detection and tracking fidelity in the face of event camera noise and robots
stopping for tests involving up to 3 robots (and upwards of 93% for 4 robots).
