Object tracking is challenging as target objects often undergo drastic
appearance changes over time. Recently, adaptive correlation filters have been
successfully applied to object tracking. However, tracking algorithms relying
on highly adaptive correlation filters are prone to drift due to noisy updates.
Moreover, as these algorithms do not maintain long-term memory of target
appearance, they cannot recover from tracking failures caused by heavy
occlusion or target disappearance in the camera view. In this paper, we propose
to learn multiple adaptive correlation filters with both long-term and
short-term memory of target appearance for robust object tracking. First, we
learn a kernelized correlation filter with an aggressive learning rate for
locating target objects precisely. We take into account the appropriate size of
surrounding context and the feature representations. Second, we learn a
correlation filter over a feature pyramid centered at the estimated target
position for predicting scale changes. Third, we learn a complementary
correlation filter with a conservative learning rate to maintain long-term
memory of target appearance. We use the output responses of this long-term
filter to determine if tracking failure occurs. In the case of tracking
failures, we apply an incrementally learned detector to recover the target
position in a sliding window fashion. Extensive experimental results on
large-scale benchmark datasets demonstrate that the proposed algorithm performs
favorably against the state-of-the-art methods in terms of efficiency,
accuracy, and robustness.

提出了一种学习多个自适应关联滤波器的方法，同时具有目标外观的长期和短期记忆，以实现强大的物体跟踪，在大规模基准数据集上表现优异，尤其在效率、准确性和鲁棒性方面均优于现有的技术方法。