Most of the existing single object trackers track the target in a unitary local search window, making them particularly vulnerable to challenging factors such as heavy occlusions and out-of-view movements. Despite the attempts to further incorporate global search, prevailing mechanisms that cooperate local and global search are relatively static, thus are still sub-optimal for improving tracking performance. By further studying the local and global search results, we raise a question: can we allow more dynamics for cooperating both results? In this paper, we propose to introduce more dynamics by devising a dynamic attention-guided multi-trajectory tracking strategy. In particular, we construct dynamic appearance model that contains multiple target templates, each of which provides its own attention for locating the target in the new frame. Guided by different attention, we maintain diversified tracking results for the target to build multi-trajectory tracking history, allowing more candidates to represent the true target trajectory. After spanning the whole sequence, we introduce a multi-trajectory selection network to find the best trajectory that delivers improved tracking performance. Extensive experimental results show that our proposed tracking strategy achieves compelling performance on various large-scale tracking benchmarks. The project page of this paper can be found at https://sites.google.com/view/mt-track/.
翻译:大多数现有单一物体追踪器都追踪单一本地搜索窗口的目标,使其特别容易受到重超和视外运动等具有挑战性的因素的影响。尽管试图进一步纳入全球搜索,但当地和全球搜索合作的现有机制相对静止,因此对于改进跟踪性能来说仍然是次最佳的。通过进一步研究本地和全球搜索结果,我们提出了一个问题:我们能否允许更多动态来合作两种结果?在本文件中,我们提议通过设计动态的注意力引导多轨跟踪战略来引入更多的动态。特别是,我们构建了包含多个目标模板的动态外观模型,每个模型都为将目标定位在新框架中提供了自己的注意力。在不同的关注指导下,我们保持了目标建立多轨跟踪历史的多样化跟踪结果,允许更多候选人代表真正的目标轨迹。在横跨整个序列之后,我们引入了一个多轨选择网络,以找到能够改进跟踪性业绩的最佳轨迹。广泛的实验结果显示,我们提议的跟踪战略在各种大规模跟踪基准上取得了令人信服的业绩。我们提出的跟踪战略可以在不同的跟踪基准上找到 http/grogalview 。