论文标题
AAA：具有理论绩效保证的任意在线跟踪器的自适应聚合
AAA: Adaptive Aggregation of Arbitrary Online Trackers with Theoretical Performance Guarantee
论文作者
论文摘要
对于视觉对象跟踪，由于目标外观的巨大变化取决于图像序列，因此很难实现全能的在线跟踪器。本文提出了一种在线跟踪方法，该方法可自适应地汇总任意多个在线跟踪器。从理论上讲，该方法的性能可以与任何图像序列的最佳跟踪器相媲美，尽管在跟踪过程中最佳专家是未知的。关于基准数据集和汇总跟踪器的较大变化的实验研究表明，所提出的方法可以实现最先进的性能。该代码可在https://github.com/songheony/aaa-journal上找到。
For visual object tracking, it is difficult to realize an almighty online tracker due to the huge variations of target appearance depending on an image sequence. This paper proposes an online tracking method that adaptively aggregates arbitrary multiple online trackers. The performance of the proposed method is theoretically guaranteed to be comparable to that of the best tracker for any image sequence, although the best expert is unknown during tracking. The experimental study on the large variations of benchmark datasets and aggregated trackers demonstrates that the proposed method can achieve state-of-the-art performance. The code is available at https://github.com/songheony/AAA-journal.
