  Jointly exploiting multiple different yet complementary domain information
has been proven to be an effective way to perform robust object tracking. This
paper focuses on effectively representing and utilizing complementary features
from the frame domain and event domain for boosting object tracking performance
in challenge scenarios. Specifically, we propose Common Features Extractor
(CFE) to learn potential common representations from the RGB domain and event
domain. For learning the unique features of the two domains, we utilize a
Unique Extractor for Event (UEE) based on Spiking Neural Networks to extract
edge cues in the event domain which may be missed in RGB in some challenging
conditions, and a Unique Extractor for RGB (UER) based on Deep Convolutional
Neural Networks to extract texture and semantic information in RGB domain.
Extensive experiments on standard RGB benchmark and real event tracking dataset
demonstrate the effectiveness of the proposed approach. We show our approach
outperforms all compared state-of-the-art tracking algorithms and verify
event-based data is a powerful cue for tracking in challenging scenes.
