  Object tracking has achieved significant progress over the past few years.
However, state-of-the-art trackers become increasingly heavy and expensive,
which limits their deployments in resource-constrained applications. In this
work, we present LightTrack, which uses neural architecture search (NAS) to
design more lightweight and efficient object trackers. Comprehensive
experiments show that our LightTrack is effective. It can find trackers that
achieve superior performance compared to handcrafted SOTA trackers, such as
SiamRPN++ and Ocean, while using much fewer model Flops and parameters.
Moreover, when deployed on resource-constrained mobile chipsets, the discovered
trackers run much faster. For example, on Snapdragon 845 Adreno GPU, LightTrack
runs $12\times$ faster than Ocean, while using $13\times$ fewer parameters and
$38\times$ fewer Flops. Such improvements might narrow the gap between academic
models and industrial deployments in object tracking task. LightTrack is
released at https://github.com/researchmm/LightTrack.
