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About this paper
Sumin Hu, Yeeun Kim, Hyungtae Lim, Alex Junho Lee, Hyun Myung
Researchers from KAIST propose eCDT, an event Clustering-based Detection and Tracking method that directly processes asynchronous event camera data for simultaneous feature detection and tracking. This approach achieves 30% longer mean feature tracks and double the median feature age compared to state-of-the-art methods, while maintaining competitive accuracy.
Key insights
- Events from a single moving edge form distinct spatio-temporal clusters, with events of opposing polarities acting as natural boundaries.
- During a brief moment of direction change, a feature's spatial position and shape remain largely intact even as its event polarity flips, allowing for linking discontinuous tracks.
- Processing raw, asynchronous event data directly, rather than converting it to frame-based representations, can better preserve the high temporal resolution and low latency of event cameras.
Summary by alphaXiv.
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