LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search
This addresses the deployment gap for resource-constrained applications in object tracking, though it is incremental as it builds on existing NAS methods.
The paper tackles the problem of heavy and expensive object trackers by using neural architecture search to design lightweight models, achieving 12x faster runtime, 13x fewer parameters, and 38x fewer Flops compared to SOTA trackers like Ocean.
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.