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Combining the Color and Event cameras (also called Dynamic Vision Sensors,DVS) for robust object tracking is a newly emerging research topic in recentyears. Existing color-event tracking framework usually contains multiplescattered modules which may lead to low efficiency and high computationalcomplexity, including feature extraction, fusion, matching, interactivelearning, etc. In this paper, we propose a single-stage backbone network forColor-Event Unified Tracking (CEUTrack), which achieves the above functionssimultaneously. Given the event points and RGB frames, we first transform thepoints into voxels and crop the template and search regions for bothmodalities, respectively. Then, these regions are projected into tokens andparallelly fed into the unified Transformer backbone network. The outputfeatures will be fed into a tracking head for target object localization. Ourproposed CEUTrack is simple, effective, and efficient, which achieves over 75FPS and new SOTA performance. To better validate the effectiveness of our modeland address the data deficiency of this task, we also propose a generic andlarge-scale benchmark dataset for color-event tracking, termed COESOT, whichcontains 90 categories and 1354 video sequences. Additionally, a new evaluationmetric named BOC is proposed in our evaluation toolkit to evaluate theprominence with respect to the baseline methods. We hope the newly proposedmethod, dataset, and evaluation metric provide a better platform forcolor-event-based tracking. The dataset, toolkit, and source code will bereleased on: \url{https://github.com/Event-AHU/COESOT}.

Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric