## 6 Conclusions  
With the increasing demand for pedestrian detection in crowded scenes, traditional pedestrian detectors face challenges due to their inability to handle ubiquitous intra-class occlusion, for which NMS struggles to accurately differentiate between the proposals of occluded pedestrians and duplicate proposals. To address the above-mentioned problem, we propose a Multi-Attribute NMS (MA-NMS) that adaptively adjusts suppression based on density and count attributes. MA-NMS strengthens suppression on duplicate proposals while weakening that for potentially occluded pedestrians, thus leading to more accurate predictions in crowded scenes. In addition, an attribute branch (ATTB) is designed to obtain the density and count attributes and simultaneously guides the adjustment of MA-NMS. ATTB uses a context extraction module (CEM) to extract the context of pedestrians, and then, concentrates the context with the feature of pedestrians for accurate attribute detection. Moreover, utilizing the proposed ATTB, a pedestrian detector based on MA-NMS is constructed, which enables more accurate predictions in crowded scenes. Extensive experiments on two challenging benchmarks are conducted, and the results demonstrate the superiority of our method compared to existing approaches, sufficiently validating the effectiveness of our model for pedestrian detection in crowded scenes. In the future, we plan to extend the application of MA-NMS to address inter-class occlusion and inter-class confusion in pedestrian detection. Additionally, a comprehensive evaluation of the sensitivity of the proposed MA-NMS is planned, and the corresponding solution is expected to be proposed in future work. Moreover, we will further attempt to use a lighter object detector to replace the two-stage Faster R-CNN, and design lightweight modules to improve the speed of the proposed MA-NMS algorithm.  
Conceptualization, W.W. and X.L. ([PERSON]); methodology, W.W. and X.L. ([PERSON]); software, W.W.; validation, W.W. and X.L. ([PERSON]); formal analysis, W.W.; investigation, W.W. and X.L. ([PERSON]); resources, W.W., T.Z. and X.L. ([PERSON]); data curation, W.W., T.Z., J.C. and S.C.; writing--original draft preparation, W.W.; writing--review and editing, W.W., T.Z. and X.L. ([PERSON]); visualization, [PERSON], J.C. and X.L. ([PERSON]); supervision, [PERSON] ([PERSON]) and X.L. ([PERSON]); project administration, X.L. ([PERSON]) and X.L. ([PERSON]); funding acquisition, X.L. ([PERSON]) and X.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Excellent Post-doctoral Program of Jiangsu Province (Grant No. 2022 ZB166), the Fundamental Research Funds for the Central Universities (Grant No. B230201007), the Project of Water Science and Technology of Jiangsu Province (Grant No. 2021080 and 2021063), the National Natural Science Foundation of China (Grant No. 42104033, 42101343, and 82004498), the Joint Fund of the Ministry of Education for Equipment Pre-research (Grant No. 8091B022123), the Research Fund from Science and Technology on Underwater Vehicle Technology Laboratory (Grant No. 2021 JCJQ-SYSJ)-LB06905), and the Qinglan Project of Jiangsu Province.  
Not applicable.  
The data presented in this study are openly available on CrowdHuman at [[https://doi.org/10.48550/arXiv.1805.00123](https://doi.org/10.48550/arXiv.1805.00123)]([https://doi.org/10.48550/arXiv.1805.00123](https://doi.org/10.48550/arXiv.1805.00123)), reference [22], and CityPersons at [[https://doi.org/10.1109/CVPR.2017.474](https://doi.org/10.1109/CVPR.2017.474)]([https://doi.org/10.1109/CVPR.2017.474](https://doi.org/10.1109/CVPR.2017.474)), reference [23].  
The authors declare no conflict of interest.