  We propose a novel memory-based tracker via part-level dense memory and
voting-based retrieval, called DMV. Since deep learning techniques have been
introduced to the tracking field, Siamese trackers have attracted many
researchers due to the balance between speed and accuracy. However, most of
them are based on a single template matching, which limits the performance as
it restricts the accessible in-formation to the initial target features. In
this paper, we relieve this limitation by maintaining an external memory that
saves the tracking record. Part-level retrieval from the memory also liberates
the information from the template and allows our tracker to better handle the
challenges such as appearance changes and occlusions. By updating the memory
during tracking, the representative power for the target object can be enhanced
without online learning. We also propose a novel voting mechanism for the
memory reading to filter out unreliable information in the memory. We
comprehensively evaluate our tracker on OTB-100,TrackingNet, GOT-10k, LaSOT,
and UAV123, which show that our method yields comparable results to the
state-of-the-art methods.
