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Most of the existing single object trackers track the target in a unitarylocal search window, making them particularly vulnerable to challenging factorssuch as heavy occlusions and out-of-view movements. Despite the attempts tofurther incorporate global search, prevailing mechanisms that cooperate localand global search are relatively static, thus are still sub-optimal forimproving tracking performance. By further studying the local and global searchresults, we raise a question: can we allow more dynamics for cooperating bothresults? In this paper, we propose to introduce more dynamics by devising adynamic attention-guided multi-trajectory tracking strategy. In particular, weconstruct dynamic appearance model that contains multiple target templates,each of which provides its own attention for locating the target in the newframe. Guided by different attention, we maintain diversified tracking resultsfor the target to build multi-trajectory tracking history, allowing morecandidates to represent the true target trajectory. After spanning the wholesequence, we introduce a multi-trajectory selection network to find the besttrajectory that delivers improved tracking performance. Extensive experimentalresults show that our proposed tracking strategy achieves compellingperformance on various large-scale tracking benchmarks. The project page ofthis paper can be found at https://sites.google.com/view/mt-track/.

Dynamic Attention guided Multi-Trajectory Analysis for Single Object Tracking