Stampede Alert Clustering Algorithmic System Based on Tiny-Scale Strengthened DETR
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917641387507712 |
|---|---|
| author | Sun, Mingze Wang, Yiqing Zhao, Zhenyi |
| author_facet | Sun, Mingze Wang, Yiqing Zhao, Zhenyi |
| contents | A novel crowd stampede detection and prediction algorithm based on Deformable DETR is proposed to address the challenges of detecting a large number of small targets and target occlusion in crowded airport and train station environments. In terms of model design, the algorithm incorporates a multi-scale feature fusion module to enlarge the receptive field and enhance the detection capability of small targets. Furthermore, the deformable attention mechanism is improved to reduce missed detections and false alarms for critical targets. Additionally, a new algorithm is innovatively introduced for stampede event prediction and visualization. Experimental evaluations on the PKX-LHR dataset demonstrate that the enhanced algorithm achieves a 34% performance in small target detection accuracy while maintaining the original detection speed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10359 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Stampede Alert Clustering Algorithmic System Based on Tiny-Scale Strengthened DETR Sun, Mingze Wang, Yiqing Zhao, Zhenyi Social and Information Networks Emerging Technologies A novel crowd stampede detection and prediction algorithm based on Deformable DETR is proposed to address the challenges of detecting a large number of small targets and target occlusion in crowded airport and train station environments. In terms of model design, the algorithm incorporates a multi-scale feature fusion module to enlarge the receptive field and enhance the detection capability of small targets. Furthermore, the deformable attention mechanism is improved to reduce missed detections and false alarms for critical targets. Additionally, a new algorithm is innovatively introduced for stampede event prediction and visualization. Experimental evaluations on the PKX-LHR dataset demonstrate that the enhanced algorithm achieves a 34% performance in small target detection accuracy while maintaining the original detection speed. |
| title | Stampede Alert Clustering Algorithmic System Based on Tiny-Scale Strengthened DETR |
| topic | Social and Information Networks Emerging Technologies |
| url | https://arxiv.org/abs/2404.10359 |