## 1 Introduction
suppression interval into strong and weak suppression intervals in order to separate the proposals of potentially occluded pedestrians and duplicate ones. Within the corresponding interval, strong or weak suppression is adaptively assigned in order to enhance the penalties of duplicate proposals while preserving the proposals of occluded pedestrians. Furthermore, suppression factors are applied to adjust the intensity of the strong and weak suppression, in order to further suppress duplicate proposals. As shown in Figure 1d, our proposed MA-NMS can effectively preserve occluded pedestrians while removing duplicate proposals. A comparison of the flows of MA-NMS and other well-known NMS algorithms is shown in Figure 2. It clearly reflects that Multi-Attribute NMS demonstrates more adaptive treatments for proposals with different duplicate probabilities. In addition, an attribute branch (ATTB) was designed, with which to obtain the density and count attributes of pedestrians, and simultaneously guide the adjustment of suppression intervals and intensity of MA-NMS. Notably, ATTB can be easily embedded into generic pedestrian detectors, which can then be used to guide the adjustment of NMS to mitigate the impact of intra-class occlusion. With the proposed ATTB, a pedestrian detector for crowded scenes was constructed based on MA-NMS, which enables more accurate predictions for pedestrian detection in crowded scenes. Moreover, the annotations of count and density attributes required for training are generated on the basis of the existing full-body annotations, without additional annotations needed. Extensive experiments were conducted using the CrowdHuman [22] and CityPersons [23] datasets. Our method delivers promising progress in crowded pedestrian detection, most notably, a 6.5% improvement in _Recall_ compared with the baseline in CrowdHuman. Our contributions can be summarized as follows:  
* In order to accurately remove duplicate detections, a Multi-Attribute NMS (MA-NMS) is proposed. Rather than using a uniform suppression interval, it refines the suppression intervals based on density attributes to perform adaptive suppression, which effectively preserves potentially occluded pedestrians, while substantially removing duplicate proposals. Additionally, the suppression intensity is further adjusted according to the count attributes, which further reduces the generation of false positives. * To obtain the density and count attributes of pedestrians, an attribute branch (ATTB) is proposed. In ATTB, a context extraction module (CME) is designed to obtain the context of pedestrians. Furthermore, it concentrates the context with the feature of pedestrians from the generic detection branch to obtain more representative feature, which allows for a more comprehensive consideration of pedestrians and their surrounding occluded pedestrians. Figure 2: Comparison of the flows of different NMS algorithms. (**a**) The flow of Greedy NMS; (**b**) the flow of Soft NMS; (**c**) the flow of Adaptive NMS; and (**d**) the flow of MA-NMS, which is proposed in this paper. With the proposed ATTB, a pedestrian detector for crowded scenes is constructed based on MA-NMS. It simultaneously considers the density and count attributes of pedestrians and adjusts the NMS based on these two attributes for more accurate pedestrian predictions in crowded scenes. The subsequent sections of the paper are organized as follows. Section 2 provides an overview of related works. Section 3 describes the fundamental flow of NMS and our method. Section 4 introduces the details and analysis of the experiments. Section 5 discusses the results, theoretical support, application scenarios, and future work. Section 6 summarizes the entire paper and possible future work.