  Current convolutional neural networks algorithms for video object tracking
spend the same amount of computation for each object and video frame. However,
it is harder to track an object in some frames than others, due to the varying
amount of clutter, scene complexity, amount of motion, and object's
distinctiveness against its background. We propose a depth-adaptive
convolutional Siamese network that performs video tracking adaptively at
multiple neural network depths. Parametric gating functions are trained to
control the depth of the convolutional feature extractor by minimizing a joint
loss of computational cost and tracking error. Our network achieves accuracy
comparable to the state-of-the-art on the VOT2016 benchmark. Furthermore, our
adaptive depth computation achieves higher accuracy for a given computational
cost than traditional fixed-structure neural networks. The presented framework
extends to other tasks that use convolutional neural networks and enables
trading speed for accuracy at runtime.
