  Deep Siamese trackers have recently gained much attention in recent years
since they can track visual objects at high speeds. Additionally, adaptive
tracking methods, where target samples collected by the tracker are employed
for online learning, have achieved state-of-the-art accuracy. However, single
object tracking (SOT) remains a challenging task in real-world application due
to changes and deformations in a target object's appearance. Learning on all
the collected samples may lead to catastrophic forgetting, and thereby corrupt
the tracking model.
  In this paper, SOT is formulated as an online incremental learning problem. A
new method is proposed for dynamic sample selection and memory replay,
preventing template corruption. In particular, we propose a change detection
mechanism to detect gradual changes in object appearance and select the
corresponding samples for online adaption. In addition, an entropy-based sample
selection strategy is introduced to maintain a diversified auxiliary buffer for
memory replay. Our proposed method can be integrated into any object tracking
algorithm that leverages online learning for model adaptation.
  Extensive experiments conducted on the OTB-100, LaSOT, UAV123, and
TrackingNet datasets highlight the cost-effectiveness of our method, along with
the contribution of its key components. Results indicate that integrating our
proposed method into state-of-art adaptive Siamese trackers can increase the
potential benefits of a template update strategy, and significantly improve
performance.

Computer Vision and Pattern Recognition