## 5 Discussion  
Dense crowds cause ubiquitous intra-class occlusion [55] between pedestrians, which is exacerbated by varying postures and different shooting angles. Consequently, dense and highly overlapped proposals are generated, posing a challenge for NMS algorithms to preserve the proposals of occluded pedestrians while removing duplicate ones. As a result, generic pedestrian detectors fail to meet the requirements of pedestrian detection in crowded scenes.  
To address the above-mentioned problem, this paper proposes a Multi-Attribute NMS (MA-NMS), which enables adaptive suppression based on the density and count attributes of pedestrians. Additionally, an attribute branch (ATTB) is proposed to obtain the density and count attributes, which guides the adjustment of suppression intervals and intensity in MA-NMS. Furthermore, leveraging the proposed ATTB, a specialized detector based on MA-NMS is constructed for pedestrian detection in crowded scenes, which incorporates the density and count attributes of pedestrians and adjusts the NMS algorithm to enhance the accuracy of pedestrian detection in crowded scenes.  
Extensive experiments are conducted on CrowdHuman and CityPersons benchmark datasets to evaluate the performance of our method. The experimental results show promising progress in crowded pedestrian detection, especially a noTable 5.2% and 6.5% improvement in _AP_ and _Recall_ compared to Greedy NMS on CrowdHuman. These findings strongly verify the advantages of the proposed MA-NMS in improving the performance of generic pedestrian detectors in crowded scenes.  
The promising progress observed in these experiments are interpretable from the perspective of previous studies and hypotheses. Recent studies [17, 18] have emphasized the importance of an appropriate suppression interval for more accurate predictions in crowded scenes. However, due to the presence of ubiquitous intra-class occlusion, determining a precise suppression interval for each pedestrian is challenging. In order to address this problem, MA-NMS introduces strong and weak suppression intervals, providing an approximation of the precise suppression interval. Additionally, inspired by Soft NMS [16], MA-NMS proposes strong and weak suppression weight functions for re-scoring in the corresponding interval. This adaptive approach enhances the adaptability of MA-NMS to complex crowded scenes, enabling more accurate predictions for pedestrian detection in crowded scenes. Moreover, inspired by [20, 21], we propose an attribute branch (ATTB) to obtain the density and count attributes for post-refining the NMS-based pedestrian detectors. The ATTB guides the adjustment of suppression intervals and intensity of MA-NMS, benefiting the accuracy of predictions from an overall perspective.  
Figure 7: Visualization of our method on CityPersons.  
MA-NMS can be easily integrated into generic pedestrian detectors based on the ATTB. By mitigating the influence of intra-class occlusion on pedestrian detectors, MA-NMS facilitates more accurate predictions for pedestrian detection in crowded scenes, in turn, having a positive impact on subsequent tasks, such as face recognition [5], pedestrian re-identification [6], and human interaction [7].  
In the future, we will utilize MA-NMS to deal with other problems in pedestrian detection, such as inter-class occlusion and inter-class confusion.