Abstract: Object tracking is a fundamental research problem in the field of computer vision.As the mainstream object tracking method sensor,conventional cameras can provide rich scene information.However,due to the limitation of sampling principle,conventional cameras suffer from overexposure or underexposure under extreme lighting conditions,and there is motion blur in high-speed motion scenes.In contrast,event camera is a bionic sensor that can sense light intensity changes to output event streams,with the advantages of high dynamic range and high temporal resolution,but it is difficult to capture static targets.Inspired by the characteristics of conventional and event cameras,a dual-modal fusion single-target tracking method,called fusion tracker,is proposed.The method adaptively fuses visual cues from conventional and event camera data by feature enhancement,while designing an attention mechanism-based feature matching network to match object cues of template frames with search frames to establish long-term feature associations and make the tracker focus on object information.The fusion tracker can solve the semantic loss problem caused by correlation operations during feature matching and improve the performance of object tra-cking.Experiments on two publicly available datasets demonstrate the superiority of our approach and validate the effectiveness of the key parts of the fusion tracker by ablation experiments.The fusion tracker can effectively improve the robustness of object tracking tasks in complex scenarios and provide reliable tracking results for downstream applications.

Key words: Object tracking, Deep learning, Event cameras, Featurefusion, Attention mechanisms