【摘 要】
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1 IntroductionrnVisual tracking has long been considered as a fundamental mis-sion in computer vision,and it has been widely used for many applications.Siamese trackers[1]utilize Siamese network to do template matching and achieve state-of-the-art perform
【机 构】
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Police Information Engineering and Network Security College,People’s Public Security University of C
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1 IntroductionrnVisual tracking has long been considered as a fundamental mis-sion in computer vision,and it has been widely used for many applications.Siamese trackers[1]utilize Siamese network to do template matching and achieve state-of-the-art performance recently.However,the siamese trackers fail to distinguish the target from the distractors with the same category and per-form unstably in complicated environment.We find there are three reasons which affect the robustness and stability of the tracker.First,visual trackers inherently have difficult to dif-ferentiate intra-class instances,and supervision from other in-dependent sources is needed.Secondly,we identify the short-comings of employing the top classification score used by cur-rent siamese trackers to filter out the region proposals.Thirdly,siamese trackers fail to utilize the inter-frame correlations of the video,which can help improve the performance of tracking.In addition,we consider the tracking process is user-intentional and target-driven,and we need to find a way to reflect it.
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