  Visual tracking can be easily disturbed by similar surrounding objects. Such
objects as hard distractors, even though being the minority among negative
samples, increase the risk of target drift and model corruption, which deserve
additional attention in online tracking and model update. To enhance the
tracking robustness, in this paper, we propose a cascaded regression tracker
with two sequential stages. In the first stage, we filter out abundant
easily-identified negative candidates via an efficient convolutional
regression. In the second stage, a discrete sampling based ridge regression is
designed to double-check the remaining ambiguous hard samples, which serves as
an alternative of fully-connected layers and benefits from the closed-form
solver for efficient learning. Extensive experiments are conducted on 11
challenging tracking benchmarks including OTB-2013, OTB-2015, VOT2018, VOT2019,
UAV123, Temple-Color, NfS, TrackingNet, LaSOT, UAV20L, and OxUvA. The proposed
method achieves state-of-the-art performance on prevalent benchmarks, while
running in a real-time speed.
