Abstract
In this paper, we propose a robust object tracking algorithm based on a branch selection mechanism to choose the most efficient object representations from multi-branch siamese networks. While most deep learning trackers use a single CNN for target representation, the proposed Multi-Branch Siamese Tracker (MBST) employs multiple branches of CNNs pre-trained for different tasks, and used for various target representations in our tracking method. With our branch selection mechanism, the appropriate CNN branch is selected depending on the target characteristics in an online manner. By using the most adequate target representation with respect to the tracked object, our method achieves real-time tracking, while obtaining improved performance compared to standard Siamese network trackers on object tracking benchmarks.
Abstract (translated)
在本文中，我们提出了一种基于分支选择机制的鲁棒对象跟踪算法，以从多分支暹罗网络中选择最有效的对象表示。虽然大多数深度学习跟踪器使用单个CNN进行目标表示，但是所提出的多分支连体跟踪器（MBST）使用针对不同任务预训练的多个CNN分支，并且在我们的跟踪方法中用于各种目标表示。利用我们的分支选择机制，以在线方式根据目标特征选择适当的CNN分支。通过使用关于被跟踪对象的最适当的目标表示，我们的方法实现了实时跟踪，同时与对象跟踪基准上的标准Siamese网络跟踪器相比获得了改进的性能。
URL
https://arxiv.org/abs/1808.07349
