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

VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows