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Compared with traditional short-term tracking, long-term tracking poses morechallenges and is much closer to realistic applications. However, few workshave been done and their performance have also been limited. In this work, wepresent a novel robust and real-time long-term tracking framework based on theproposed skimming and perusal modules. The perusal module consists of aneffective bounding box regressor to generate a series of candidate proposalsand a robust target verifier to infer the optimal candidate with its confidencescore. Based on this score, our tracker determines whether the tracked objectbeing present or absent, and then chooses the tracking strategies of localsearch or global search respectively in the next frame. To speed up theimage-wide global search, a novel skimming module is designed to efficientlychoose the most possible regions from a large number of sliding windows.Numerous experimental results on the VOT-2018 long-term and OxUvA long-termbenchmarks demonstrate that the proposed method achieves the best performanceand runs in real-time. The source codes are available athttps://github.com/iiau-tracker/SPLT.

'Skimming-Perusal' Tracking: A Framework for Real-Time and Robust Long-term Tracking