  Different from visible cameras which record intensity images frame by frame,
the biologically inspired event camera produces a stream of asynchronous and
sparse events with much lower latency. In practice, visible cameras can better
perceive texture details and slow motion, while event cameras can be free from
motion blurs and have a larger dynamic range which enables them to work well
under fast motion and low illumination. Therefore, the two sensors can
cooperate with each other to achieve more reliable object tracking. In this
work, we propose a large-scale Visible-Event benchmark (termed VisEvent) due to
the lack of a realistic and scaled dataset for this task. Our dataset consists
of 820 video pairs captured under low illumination, high speed, and background
clutter scenarios, and it is divided into a training and a testing subset, each
of which contains 500 and 320 videos, respectively. Based on VisEvent, we
transform the event flows into event images and construct more than 30 baseline
methods by extending current single-modality trackers into dual-modality
versions. More importantly, we further build a simple but effective tracking
algorithm by proposing a cross-modality transformer, to achieve more effective
feature fusion between visible and event data. Extensive experiments on the
proposed VisEvent dataset, FE108, COESOT, and two simulated datasets (i.e.,
OTB-DVS and VOT-DVS), validated the effectiveness of our model. The dataset and
source code have been released on:
\url{https://github.com/wangxiao5791509/VisEvent_SOT_Benchmark}.

Computer Vision and Pattern Recognition;Artificial Intelligence