  During the last years, deep learning trackers achieved stimulating results
while bringing interesting ideas to solve the tracking problem. This progress
is mainly due to the use of learned deep features obtained by training deep
convolutional neural networks (CNNs) on large image databases. But since CNNs
were originally developed for image classification, appearance modeling
provided by their deep layers might be not enough discriminative for the
tracking task. In fact,such features represent high-level information, that is
more related to object category than to a specific instance of the object.
Motivated by this observation, and by the fact that discriminative correlation
filters(DCFs) may provide a complimentary low-level information, we presenta
novel tracking algorithm taking advantage of both approaches. We formulate the
tracking task as a two-stage procedure. First, we exploit the generalization
ability of deep features to coarsely estimate target translation, while
ensuring invariance to appearance change. Then, we capitalize on the
discriminative power of correlation filters to precisely localize the tracked
object. Furthermore, we designed an update control mechanism to learn
appearance change while avoiding model drift. We evaluated the proposed tracker
on object tracking benchmarks. Experimental results show the robustness of our
algorithm, which performs favorably against CNN and DCF-based trackers. Code is
available at: https://github.com/AhmedZgaren/Coarse-to-fine-Tracker
