  In this work, a novel high-speed single object tracker that is robust against
non-semantic distractor exemplars is introduced; dubbed BOBBY2. It incorporates
a novel exemplar buffer module that sparsely caches the target's appearance
across time, enabling it to adapt to potential target deformation. As for
training, an augmented ImageNet-VID dataset was used in conjunction with the
one cycle policy, enabling it to reach convergence with less than 2 epoch worth
of data. For validation, the model was benchmarked on the GOT-10k dataset and
on an additional small, albeit challenging custom UAV dataset collected with
the TU-3 UAV. We demonstrate that the exemplar buffer is capable of providing
redundancies in case of unintended target drifts, a desirable trait in any
middle to long term tracking. Even when the buffer is predominantly filled with
distractors instead of valid exemplars, BOBBY2 is capable of maintaining a
near-optimal level of accuracy. BOBBY2 manages to achieve a very competitive
result on the GOT-10k dataset and to a lesser degree on the challenging custom
TU-3 dataset, without fine-tuning, demonstrating its generalizability. In terms
of speed, BOBBY2 utilizes a stripped down AlexNet as feature extractor with 63%
less parameters than a vanilla AlexNet, thus being able to run at a competitive
85 FPS.
