  Occlusion is a long-standing problem that causes many modern tracking methods
to be erroneous. In this paper, we address the occlusion problem by exploiting
the current and future possible locations of the target object from its past
trajectory. To achieve this, we introduce a learning-based tracking method that
takes into account background motion modeling and trajectory prediction. Our
trajectory prediction module predicts the target object's locations in the
current and future frames based on the object's past trajectory. Since, in the
input video, the target object's trajectory is not only affected by the object
motion but also the camera motion, our background motion module estimates the
camera motion. So that the object's trajectory can be made independent from it.
To dynamically switch between the appearance-based tracker and the trajectory
prediction, we employ a network that can assess how good a tracking prediction
is, and we use the assessment scores to choose between the appearance-based
tracker's prediction and the trajectory-based prediction. Comprehensive
evaluations show that the proposed method sets a new state-of-the-art
performance on commonly used tracking benchmarks.
