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, the 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, 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 at our project page:
https://sites.google.com/view/viseventtrack/.

本文提出了一个大规模的可见事件跟踪基准测试（VisEvent），由 820 个视频对组成，其中包含以低照度、高速和背景杂波为特点的数据集，将事件流转换为事件图像，并构建了 30 多种基线算法和一个简单但有效的跨模态转换器，以实现可见数据和事件数据之间的更有效特征融合。实验结果验证了模型的有效性。