  In this paper, we design a tracking model consisting of response generation
and bounding box regression, where the first component produces a heat map to
indicate the presence of the object at different positions and the second part
regresses the relative bounding box shifts to anchors mounted on sliding-window
locations. Thanks to the resizable convolutional filters used in both
components to adapt to the shape changes of objects, our tracking model does
not need to enumerate different sized anchors, thus saving model parameters. To
effectively adapt the model to appearance variations, we propose to offline
train a recurrent neural optimizer to update tracking model in a meta-learning
setting, which can converge the model in a few gradient steps. This improves
the convergence speed of updating the tracking model while achieving better
performance. We extensively evaluate our trackers, ROAM and ROAM++, on the OTB,
VOT, LaSOT, GOT-10K and TrackingNet benchmark and our methods perform favorably
against state-of-the-art algorithms.
