  In comparison to conventional RGB cameras, the superior temporal resolution
of event cameras allows them to capture rich information between frames, making
them prime candidates for object tracking. Yet in practice, despite their
theoretical advantages, the body of work on event-based multi-object tracking
(MOT) remains in its infancy, especially in real-world settings where events
from complex background and camera motion can easily obscure the true target
motion. In this work, an event-based multi-object tracker, called SpikeMOT, is
presented to address these challenges. SpikeMOT leverages spiking neural
networks to extract sparse spatiotemporal features from event streams
associated with objects. The resulting spike train representations are used to
track the object movement at high frequency, while a simultaneous object
detector provides updated spatial information of these objects at an equivalent
frame rate. To evaluate the effectiveness of SpikeMOT, we introduce DSEC-MOT,
the first large-scale event-based MOT benchmark incorporating fine-grained
annotations for objects experiencing severe occlusions, frequent trajectory
intersections, and long-term re-identification in real-world contexts.
Extensive experiments employing DSEC-MOT and another event-based dataset, named
FE240hz, demonstrate SpikeMOT's capability to achieve high tracking accuracy
amidst challenging real-world scenarios, advancing the state-of-the-art in
event-based multi-object tracking.
