  We present a novel algorithm utilizing a deep Siamese neural network as a
general object similarity function in combination with a Bayesian optimization
(BO) framework to encode spatio-temporal information for efficient object
tracking in video. In particular, we treat the video tracking problem as a
dynamic (i.e. temporally-evolving) optimization problem. Using Gaussian Process
priors, we model a dynamic objective function representing the location of a
tracked object in each frame. By exploiting temporal correlations, the proposed
method queries the search space in a statistically principled and efficient
way, offering several benefits over current state of the art video tracking
methods.
