  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, iv) the speed of the robots, and
v) variation in ambient light conditions 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). When the lighting
conditions were varied, a graceful degradation in detection and tracking
fidelity was observed.
