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Visual tracking problem demands to efficiently perform robust classificationand accurate target state estimation over a given target at the same time.Former methods have proposed various ways of target state estimation, yet fewof them took the particularity of the visual tracking problem itself intoconsideration. After a careful analysis, we propose a set of practicalguidelines of target state estimation for high-performance generic objecttracker design. Following these guidelines, we design our Fully ConvolutionalSiamese tracker++ (SiamFC++) by introducing both classification and targetstate estimation branch(G1), classification score without ambiguity(G2),tracking without prior knowledge(G3), and estimation quality score(G4).Extensive analysis and ablation studies demonstrate the effectiveness of ourproposed guidelines. Without bells and whistles, our SiamFC++ tracker achievesstate-of-the-art performance on five challenging benchmarks(OTB2015, VOT2018,LaSOT, GOT-10k, TrackingNet), which proves both the tracking and generalizationability of the tracker. Particularly, on the large-scale TrackingNet dataset,SiamFC++ achieves a previously unseen AUC score of 75.4 while running at over90 FPS, which is far above the real-time requirement. Code and models areavailable at: https://github.com/MegviiDetection/video_analyst .

SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines