Current event-/frame-event based trackers undergo evaluation on short-term tracking datasets, however, the tracking of real-world scenarios involves long-term tracking, and the performance of existing tracking algorithms in these scenarios remains unclear. In this paper, we first propose a new long-term and large-scale frame-event single object tracking dataset, termed FELT. It contains 742 videos and 1,594,474 RGB frames and event stream pairs and has become the largest frame-event tracking dataset to date. We re-train and evaluate 15 baseline trackers on our dataset for future works to compare. More importantly, we find that the RGB frames and event streams are naturally incomplete due to the influence of challenging factors and spatially sparse event flow. In response to this, we propose a novel associative memory Transformer network as a unified backbone by introducing modern Hopfield layers into multi-head self-attention blocks to fuse both RGB and event data. Extensive experiments on both FELT and RGB-T tracking dataset LasHeR fully validated the effectiveness of our model. The dataset and source code can be found at \url{https://github.com/Event-AHU/FELT_SOT_Benchmark}.

提出了一个新的长期和大规模帧事件单目标跟踪数据集（FELT），该数据集包含742个视频和1,594,474个RGB帧和事件流对，已成为迄今为止最大的帧事件跟踪数据集。通过引入现代Hopfield层到多头自注意力块中的联想记忆Transformer网络来融合RGB和事件数据，验证了模型的有效性。

长期框架-事件视觉跟踪：基准数据集和基线