arXiv：cs.LG· Pedro Manoel Herminio Alves, Dalton C\'ezane Gomes Valadares, Cl\'audio de Souza Baptista·· 3 小时前
检测器架构、模型规模与场景密度如何影响多行人跟踪
The Impact of Detector Architecture, Model Scale, and Scene Density on Multi-Pedestrian Tracking
AI 导读
一篇 arXiv 论文对比了 YOLO11、RT-DETR、YOLO26 检测器家族与 ByteTrack、BoT-SORT 跟踪器在 MOT17、MOT20、SOMPT22 基准上的多行人跟踪表现。
正文
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 architecture, model scale, scene density, and inference regime affect the tracking of multiple pedestrians. We compare the YOLO11, RT-DETR, and YOLO26 detector families with the ByteTrack and BoT-SORT trackers on sequences from the MOT17, MOT20, and SOMPT22 benchmarks. For YOLO26, we also compare traditional overlap filtering via Non-Maximum Suppression (NMS) with end-to-end inference. The RT-DETR family achieved the highest mean scores on the HOTA tracking quality metric across all three benchmarks with both trackers, with a more pronounced advantage on MOT20, albeit at a higher computational cost. On MOT17 and SOMPT22, YOLO26 variants, especially YOLO26-L with NMS, offered favorable combinations of quality and speed compared with RT-DETR. In the YOLO26 inference comparison, the end-to-end mode did not yield consistent tracking gains over the NMS-based regime. Taken together, these results provide a practical guide for selecting detectors and trackers for this task based on application priorities.
| Comments: | 6 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.19369 [cs.CV] |
| (or arXiv:2610.19369v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19369 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Dalton Valadares [view email]
[v1]
Wed, 14 Oct 2026 01:12:58 UTC (2,693 KB)
来源：arXiv：cs.LG · arxiv.org