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3D hand tracking methods based on monocular RGB videos are easily affected bymotion blur, while event camera, a sensor with high temporal resolution anddynamic range, is naturally suitable for this task with sparse output and lowpower consumption. However, obtaining 3D annotations of fast-moving hands isdifficult for constructing event-based hand-tracking datasets. In this paper,we provided an event-based speed adaptive hand tracker (ESAHT) to solve thehand tracking problem based on event camera. We enabled a CNN model trained ona hand tracking dataset with slow motion, which enabled the model to leveragethe knowledge of RGB-based hand tracking solutions, to work on fast handtracking tasks. To realize our solution, we constructed the first 3D handtracking dataset captured by an event camera in a real-world environment,figured out two data augment methods to narrow the domain gap between slow andfast motion data, developed a speed adaptive event stream segmentation methodto handle hand movements in different moving speeds, and introduced a newevent-to-frame representation method adaptive to event streams with differentlengths. Experiments showed that our solution outperformed RGB-based as well asprevious event-based solutions in fast hand tracking tasks, and our codes anddataset will be publicly available.

Tracking Fast by Learning Slow: An Event-based Speed Adaptive Hand Tracker Leveraging Knowledge in RGB Domain