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In comparison to conventional RGB cameras, the superior temporal resolutionof event cameras allows them to capture rich information between frames, makingthem prime candidates for object tracking. Yet in practice, despite theirtheoretical advantages, the body of work on event-based multi-object tracking(MOT) remains in its infancy, especially in real-world settings where eventsfrom complex background and camera motion can easily obscure the true targetmotion. In this work, an event-based multi-object tracker, called SpikeMOT, ispresented to address these challenges. SpikeMOT leverages spiking neuralnetworks to extract sparse spatiotemporal features from event streamsassociated with objects. The resulting spike train representations are used totrack the object movement at high frequency, while a simultaneous objectdetector provides updated spatial information of these objects at an equivalentframe rate. To evaluate the effectiveness of SpikeMOT, we introduce DSEC-MOT,the first large-scale event-based MOT benchmark incorporating fine-grainedannotations for objects experiencing severe occlusions, frequent trajectoryintersections, and long-term re-identification in real-world contexts.Extensive experiments employing DSEC-MOT and another event-based dataset, namedFE240hz, demonstrate SpikeMOT's capability to achieve high tracking accuracyamidst challenging real-world scenarios, advancing the state-of-the-art inevent-based multi-object tracking.

SpikeMOT: Event-based Multi-Object Tracking with Sparse Motion Features