Abstract. In the field of generic object tracking numerous attempts
have been made to exploit deep features. Despite all expectations, deep
trackers are yet to reach an outstanding level of performance compared
to methods solely based on handcrafted features. In this paper, we investigate this key issue and propose an approach to unlock the true potential
of deep features for tracking. We systematically study the characteristics
of both deep and shallow features, and their relation to tracking accuracy
and robustness. We identify the limited data and low spatial resolution as
the main challenges, and propose strategies to counter these issues when
integrating deep features for tracking. Furthermore, we propose a novel
adaptive fusion approach that leverages the complementary properties
of deep and shallow features to improve both robustness and accuracy.
Extensive experiments are performed on four challenging datasets. On
VOT2017, our approach significantly outperforms the top performing
tracker from the challenge with a relative gain of 17% in EAO