  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.
