  Feature encoding with respect to an over-complete dictionary learned by
unsupervised methods, followed by spatial pyramid pooling, and linear
classification, has exhibited powerful strength in various vision applications.
Here we propose to use the feature learning pipeline for visual tracking.
Tracking is implemented using tracking-by-detection and the resulted framework
is very simple yet effective. First, online dictionary learning is used to
build a dictionary, which captures the appearance changes of the tracking
target as well as the background changes. Given a test image window, we extract
local image patches from it and each local patch is encoded with respect to the
dictionary. The encoded features are then pooled over a spatial pyramid to form
an aggregated feature vector. Finally, a simple linear classifier is trained on
these features.
  Our experiments show that the proposed powerful---albeit simple---tracker,
outperforms all the state-of-the-art tracking methods that we have tested.
Moreover, we evaluate the performance of different dictionary learning and
feature encoding methods in the proposed tracking framework, and analyse the
impact of each component in the tracking scenario. We also demonstrate the
flexibility of feature learning by plugging it into Hare et al.'s tracking
method. The outcome is, to our knowledge, the best tracker ever reported, which
facilitates the advantages of both feature learning and structured output
prediction.
