English(EN) The Impact of Detector Architecture, Model Scale, and Scene Density on Multi-Pedestrian Tracking

YOLO vs RT-DETR：行人跟踪性能对比

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

一篇新的研究论文评估了在拥挤场景下，各种目标检测架构和跟踪算法在多行人跟踪方面的性能。该研究在MOT17、MOT20和SOMPT22基准上，比较了YOLO11、RT-DETR和YOLO26检测器系列与ByteTrack和BoT-SORT跟踪器。在MOT20上，RT-DETR展现了卓越的跟踪质量；而在MOT17和SOMPT22上，YOLO26系列在速度和质量之间提供了更好的平衡。研究还发现，对于YOLO26，端到端推理并未比传统的非极大值抑制（NMS）带来持续的跟踪改进。 AI

影响根据具体的应用需求和计算限制，为选择最优目标检测和跟踪系统提供指导。

排序理由研究论文，详细介绍了计算机视觉模型和算法的比较分析。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Pedro Manoel Herminio Alves, Dalton C\'ezane Gomes Valadares, Cl\'audio de Souza Baptista · 2026-10-15 04:00

检测器架构、模型规模和场景密度对多行人跟踪的影响

arXiv:2610.19369v1 Announce Type: cross Abstract: Pedestrian tracking in video is important for applications such as surveillance and scene analysis, but it becomes more challenging in crowded scenarios with frequent occlusions and rapid motion. This paper analyzes how detector a…

本文如何被排名

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检测器架构、模型规模和场景密度对多行人跟踪的影响

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