  The speed-precision trade-off is a critical problem for visual object
tracking which usually requires low latency and deployment on constrained
resources. Existing solutions for efficient tracking mainly focus on adopting
light-weight backbones or modules, which nevertheless come at the cost of a
sacrifice in precision. In this paper, inspired by dynamic network routing, we
propose DyTrack, a dynamic transformer framework for efficient tracking.
Real-world tracking scenarios exhibit diverse levels of complexity. We argue
that a simple network is sufficient for easy frames in video sequences, while
more computation could be assigned to difficult ones. DyTrack automatically
learns to configure proper reasoning routes for various inputs, gaining better
utilization of the available computational budget. Thus, it can achieve higher
performance with the same running speed. We formulate instance-specific
tracking as a sequential decision problem and attach terminating branches to
intermediate layers of the entire model. Especially, to fully utilize the
computations, we introduce the feature recycling mechanism to reuse the outputs
of predecessors. Furthermore, a target-aware self-distillation strategy is
designed to enhance the discriminating capabilities of early predictions by
effectively mimicking the representation pattern of the deep model. Extensive
experiments on multiple benchmarks demonstrate that DyTrack achieves promising
speed-precision trade-offs with only a single model. For instance, DyTrack
obtains 64.9% AUC on LaSOT with a speed of 256 fps.
