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.

本研究提出了一种深度自适应的卷积 Siamese 网络，使用参数门控函数控制卷积的深度以平衡跟踪精度和计算成本，在 VOT2016 基准测试上取得了可比较的精度。同时，对比传统的固定结构神经网络，在给定计算成本下取得了更高的精度，该框架可以在卷积神经网络的其他任务中使用，并使运行时的速度与准确性交换。