  We propose a novel meta-learning framework for real-time object tracking with
efficient model adaptation and channel pruning. Given an object tracker, our
framework learns to fine-tune its model parameters in only a few iterations of
gradient-descent during tracking while pruning its network channels using the
target ground-truth at the first frame. Such a learning problem is formulated
as a meta-learning task, where a meta-tracker is trained by updating its
meta-parameters for initial weights, learning rates, and pruning masks through
carefully designed tracking simulations. The integrated meta-tracker greatly
improves tracking performance by accelerating the convergence of online
learning and reducing the cost of feature computation. Experimental evaluation
on the standard datasets demonstrates its outstanding accuracy and speed
compared to the state-of-the-art methods.
