GAP-Track: Bridging the Resolution Gap for Cross-Resolution RGBT Tracking

RGBT tracking on edge devices is often constrained by lim-ited computational power and data transmission bandwidth, necessitat-ing the use of low-resolution inputs in real-world deployments. However,such resolution reduction typically leads to severe semantic loss and per-formance degradation. In this paper, we propose GAP-Track, an efficientframework that bridges the resolution gap by enabling high-precisiontracking of low-resolution inputs. Specifically, we introduce a hierarchicalknowledge distillation strategy that guides the low-resolution student bymulti-level teacher supervision. To recover the missing semantic details,a generative reconstruction module trained by a random masking strat-egy is introduced to strengthen the student’s feature representation byforcing the backbone to recover fine-grained structural information fromlow resolution data. Furthermore, we adapt a polar-geometric sensitivityloss that transforms the bounding box regression into a polar coordinatesystem, effectively mitigating localization ambiguities in extremely low-resolution scenarios by providing higher sensitivity to center-point driftand shape deformations. Extensive experiments on the comprehensiveLasHeR and RGBT234 datasets demonstrate that our method achievesstate-of-the-art performance. Notably, GAP-Track delivers superior pre-cision at 1/2 resolution and continues to significantly outperform thebaseline even at 1/4 resolution, ensuring a robust balance between track-ing accuracy and inference efficiency. The source code will be availableat https://github.com/shijiahahaha/GAPTrack.