  Most existing RGB-based trackers target low frame rate benchmarks of around
30 frames per second. This setting restricts the tracker's functionality in the
real world, especially for fast motion. Event-based cameras as bioinspired
sensors provide considerable potential for high frame rate tracking due to
their high temporal resolution. However, event-based cameras cannot offer
fine-grained texture information like conventional cameras. This unique
complementarity motivates us to combine conventional frames and events for high
frame rate object tracking under various challenging conditions. Inthispaper,
we propose an end-to-end network consisting of multi-modality alignment and
fusion modules to effectively combine meaningful information from both
modalities at different measurement rates. The alignment module is responsible
for cross-style and cross-frame-rate alignment between frame and event
modalities under the guidance of the moving cues furnished by events. While the
fusion module is accountable for emphasizing valuable features and suppressing
noise information by the mutual complement between the two modalities.
Extensive experiments show that the proposed approach outperforms
state-of-the-art trackers by a significant margin in high frame rate tracking.
With the FE240hz dataset, our approach achieves high frame rate tracking up to
240Hz.
