Visual object tracking is a significant computer vision task which can be
applied to many domains such as visual surveillance, human computer
interaction, and video compression. In the literature, researchers have
proposed a variety of 2D appearance models. To help readers swiftly learn the
recent advances in 2D appearance models for visual object tracking, we
contribute this survey, which provides a detailed review of the existing 2D
appearance models. In particular, this survey takes a module-based architecture
that enables readers to easily grasp the key points of visual object tracking.
In this survey, we first decompose the problem of appearance modeling into two
different processing stages: visual representation and statistical modeling.
Then, different 2D appearance models are categorized and discussed with respect
to their composition modules. Finally, we address several issues of interest as
well as the remaining challenges for future research on this topic. The
contributions of this survey are four-fold. First, we review the literature of
visual representations according to their feature-construction mechanisms
(i.e., local and global). Second, the existing statistical modeling schemes for
tracking-by-detection are reviewed according to their model-construction
mechanisms: generative, discriminative, and hybrid generative-discriminative.
Third, each type of visual representations or statistical modeling techniques
is analyzed and discussed from a theoretical or practical viewpoint. Fourth,
the existing benchmark resources (e.g., source code and video datasets) are
examined in this survey.

本文考虑了 2D 表现模型在视觉目标跟踪中的应用，针对其处理阶段的不同阶段给出分类，并就其从理论和实用角度进行了分析和讨论；本文对此问题未来研究提出了几点挑战。