SmoC-Track: Smoothing Observation Noise and Feature Degradation for Robust Multi-Object Tracking

Abstract

In complex and crowded scenarios, Multiple Object Tracking (MOT) frequently suffers from severe observation noise and feature degradation induced by frequent target occlusion and motion blur. Existing Tracking-by-Detection (TBD) paradigms typically employ detection confidence thresholds for multi-stage data association, making them highly susceptible to trajectory fragmentation and identity switches (IDSW) among high-quality tracklets. To address these issues, we propose SmoC-Track, a robust MOT approach that dynamically models track confidence to integrate reliable confidence cues as prior constraints into the data association process. First, a multi-dimensional Track Confidence Modeling (TCM) module is proposed to effectively smooth out feature degradation noise during occlusions by fusing long-term historical accumulated states with continuous temporal association features. Second, based on the modeled confidence, we design the Confidence-Height Joint Intersection over Union (CHIoU), a confidence-guided adaptive boundary buffer metric. This mechanism comprises two synergistic components: CIoU adaptively expands the matching search space based on track confidence, whereas HIoU explicitly leverages the invariance prior of bounding box heights in crowded scenes to achieve precise identity decoupling among highly overlapping candidates. Extensive experiments demonstrate that SmoC-Track achieves a HOTA of 66.4% and an IDF1 of 81.8% on the MOT17 test set, as well as 66.9% and 83.1% on the MOT20 test set. These results comprehensively validate its robustness and effectiveness in complex scenarios.