ARUDet: active retrieval and uncertainty-aware detection for sports video object detection

Video object detection in sports scenarios faces severe challenges posed by high speed motion, heavy occlusion, and complex deformation. Existing methods employ passive feature aggregation that applies a uniform fusion strategy across all regions, neglecting the type specific differences of local degradation and failing to match targeted complementary information for different defects. Meanwhile, visual degradation causes object boundaries to exhibit probabilistic distribution characteristics, yet conventional deterministic regression ignores such geometric ambiguity and forces fitting a single coordinate, making localization reliability difficult to guarantee. To address these issues, we propose ARUDet (Active Retrieval Uncertainty-aware Detector), comprising an Active Temporal Retrieval Module (ATRM) and an Uncertainty Rectified Regression Head (URH). Specifically, ATRM first identifies the degradation type of each region in the current frame through a Defect Responsive Assessor and encodes it into an explicit query vector. Subsequently, under dual constraints of semantic consistency and quality complementarity, it actively retrieves the best matching historical feature patches from a temporal memory bank and selectively replaces only low quality regions, achieving on demand restoration. URH collaboratively suppresses geometric uncertainty through a Probabilistic Boundary Projector (PBP) and a Lower Bound Optimizer (LBO): the former explicitly models the geometric distribution to improve localization quality, while the latter constrains the worst case error to tighten the prediction lower bound. Experiments on multiple sports datasets demonstrate that ARUDet achieves significant performance improvements, further validating the effectiveness of the proposed method.