  This survey presents a deep analysis of the learning and inference
capabilities in nine popular trackers. It is neither intended to study the
whole literature nor is it an attempt to review all kinds of neural networks
proposed for visual tracking. We focus instead on Siamese neural networks which
are a promising starting point for studying the challenging problem of
tracking. These networks integrate efficiently feature learning and the
temporal matching and have so far shown state-of-the-art performance. In
particular, the branches of Siamese networks, their layers connecting these
branches, specific aspects of training and the embedding of these networks into
the tracker are highlighted. Quantitative results from existing papers are
compared with the conclusion that the current evaluation methodology shows
problems with the reproducibility and the comparability of results. The paper
proposes a novel Lisp-like formalism for a better comparison of trackers. This
assumes a certain functional design and functional decomposition of trackers.
The paper tries to give foundation for tracker design by a formulation of the
problem based on the theory of machine learning and by the interpretation of a
tracker as a decision function. The work concludes with promising lines of
research and suggests future work.
