  In this paper, we propose and study a novel visual object tracking approach
based on convolutional networks and recurrent networks. The proposed approach
is distinct from the existing approaches to visual object tracking, such as
filtering-based ones and tracking-by-detection ones, in the sense that the
tracking system is explicitly trained off-line to track anonymous objects in a
noisy environment. The proposed visual tracking model is end-to-end trainable,
minimizing any adversarial effect from mismatches in object representation and
between the true underlying dynamics and learning dynamics. We empirically show
that the proposed tracking approach works well in various scenarios by
generating artificial video sequences with varying conditions; the number of
objects, amount of noise and the match between the training shapes and test
shapes.
