Cluster-NMS: Improving Crowded Object Detection through Clustering Pattern

Crowded object detection is a highly challenging task in the field of object detection. One major issue is that the standard NMS algorithm encounters difficulties in crowded scenes, as it often erroneously suppresses highly overlapped predictions. In fact, if there are highly clustered bounding boxe...

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Veröffentlicht in:Signal, image and video processing Jg. 19; H. 9; S. 758
Hauptverfasser: Liao, Junguo, Tian, Haonan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.09.2025
Springer Nature B.V
Schlagworte:
Accuracy
Algorithms
Boxes
Clustering
Computer Imaging
Computer Science
Confidence
Datasets
Image Processing and Computer Vision
Multimedia Information Systems
Original Paper
Pattern Recognition and Graphics
Pedestrians
Sensors
Signal,Image and Speech Processing
Vision
Anchor-based object detectors
Crowded scenes
Non-maximum suppression
Object detection
ISSN:1863-1703, 1863-1711
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Zusammenfassung:Crowded object detection is a highly challenging task in the field of object detection. One major issue is that the standard NMS algorithm encounters difficulties in crowded scenes, as it often erroneously suppresses highly overlapped predictions. In fact, if there are highly clustered bounding boxes in a region, they usually correspond to a ground-truth object. Based on this pattern, we propose an effective cluster-NMS algorithm which can enhance the confidence of not being suppressed for a highly scored bounding box by taking into account the surrounding clustered boxes. It ensures that true predictions are correctly retained in the non-maximum suppression step. On the CrowdHuman dataset, cluster-NMS outperforms other NMS algorithms in improving the performance of crowded object detection. The YOLOv7 detector with cluster-NMS achieves 91.4% AP, which represents the best performance among the crowded object detectors evaluated in the comparative experiments. Furthermore, reliable improvements on CityPersons datasets demonstrate the robustness of our method.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-025-04374-3