## 1 Introduction  
Pedestrian detection [1] is a popular research topic in computer vision that has been widely applied in automatic driving [2], video surveillance [3], robotics [4], etc. As a fundamental task, pedestrian detection drives the development of research, such as face recognition [5], pedestrian re-identification [6], and human interaction [7]. With the wide spread of convolutional neural networks, numerous object detectors [8, 9, 10] have been proposed based on deep learning features, some of which have been applied to pedestrian detection after fine-tuning. Furthermore, several proposed pedestrian detectors [11, 12, 13]have attempted to detect pedestrians using high-level semantic features [14; 15] from another perspective. In order to obtain all the pedestrians in the images, most of the above-mentioned pedestrian detectors are dense detectors, which generate multiple proposals for each pedestrian. However, each pedestrian is represented by only one optimal proposal in the final predictions, for which NMS is widely used to remove duplicates. NMS was originally implemented via the greedy algorithm, which is known as Greedy NMS. For each pedestrian, it uses a rigid threshold in order to divide a uniform suppression interval, and directly discards all other proposals within the interval to remove the duplicates, which satisfied the need for pedestrian detection in sparse scenes in early studies. Recently, as artificial intelligence [7] has gradually penetrated our lives, the demand for pedestrian detection in more crowded scenes, such as shopping malls, railway stations, airports, and streets, has increased. However, dense crowds and various shooting angles mean the pedestrians in the images are heavily occluded by each other, resulting in highly similar proposals between pedestrians and their surrounding occluded pedestrians, and leading Greedy NMS to mistakenly consider the proposals of occluded pedestrians as duplicate ones, and then, discard them. The above-mentioned problem finally means that the detectors fail to meet the demand for pedestrian detection in crowded scenes, as shown in Figure 1a. Recent studies [16; 17; 18] have attempted to soften suppression in order to decrease the tendency of NMS to incorrectly remove proposals belonging to occluded pedestrians. Moreover, the authors of [19; 20; 21] attempted to remove duplicate proposals according to the distance of their attributes from others. Though significant breakthroughs have been made, numerous false positives are generated, and highly occluded pedestrians continue to be missed, as shown in Figure 1. We compared the current NMS algorithms and found the following common drawbacks:  
* For each pedestrian, only a rigid or a dynamic threshold is used to divide the single suppression interval, and all the proposals within the interval are considered duplicates;
* A uniform suppression operation is applied in the suppression interval, such as discarding, or a suppression weight function for re-scoring, making it more difficult to remove the highly similar duplicate proposals of pedestrians while preserving the proposals of occluded pedestrians. The above-mentioned drawbacks make it difficult for the current NMS algorithms to remove various duplicate proposals that are too close to or distant from the annotations, and retain the proposals of occluded pedestrians at the same time. Thus, there is still room for research on making an NMS that can accurately preserve occluded pedestrians while removing duplicate proposals. In this paper, we propose a Multi-Attribute NMS (MA-NMS), which adaptively adjusts suppression based on density and count attributes. It refines the traditional single  
Figure 1: Results of different NMS algorithms. The yellow dashed boxes show the occluded pedestrians that are incorrectly removed, and the red dashed boxes highlight the false positives mistakenly retained by NMS. (**a**) The result of Greedy NMS; (**b**) the result of Soft NMS; (**c**) the result of Adaptive NMS; and (**d**) the result of MA-NMS (ours).